The growing demand for security screening in public spaces necessitates more efficient and reliable X-ray baggage inspection systems. Current manual interpretation of X-ray images at stations，airports，and stadium venues risks missing prohibited items due to operator fatigue. This method also requires considerable human resources，leading to high operational costs. Intelligent X-ray image detection technology，using computer-assisted verification，enhances security check reliability，improves efficiency，and reduces operational costs. Intelligent X-ray detection methods fall into three categories based on imaging approaches： single view，dual view，and CT scanning. Dual-view systems，adding an orthogonal X-ray image to complement single-view inspection，offer improved detection performance by revealing occluded details. This approach achieves an optimal trade-off between detection capability and cost compared with CT scanners. Research on dual-view X-ray prohibited item detection has focused on leveraging the spatial correspondence between views to enhance model performance. Some studies have adopted a primary-auxiliary view strategy. In this strategy，the auxiliary view enhances feature representation in the primary view，somewhat improving detection accuracy. However，this asymmetric fusion approach only considers predictions from the primary view，which can potentially compromise accuracy in complex scenarios where prohibited items are poorly angled or heavily occluded in the primary view. Other researchers have developed symmetric multiscale architectures to boost detection performance through deep dual-view information fusion. However，these methods employ single-column feature fusion，limiting the exploration of potential correlations between feature columns at different scales. Such a limitation may affect the model’s ability to recognize prohibited items of varying sizes and its overall robustness. The challenge remains in developing more effective dual-view feature fusion strategies. These strategies are needed to address complex screening scenarios and improve detection performance across different sizes of prohibited items.

To tackle this challenge，this study presents a dual-view prohibited item detection model，extending the RT-DETR single-channel architecture to a dual-channel network. In the encoder，features are fed into a dual-view multiscale weighted fusion module. This module enhances feature extraction for different-sized prohibited items through multisize sliding windows and establishes spatial correspondence between dual-view features using a cross-attention mechanism. A gated feature fusion mechanism then integrates dual-channel features to strengthen the encoder’s semantic representation. In the decoder，we introduce a 3D anchor-guided localization fusion module to mitigate occlusion. The core of this approach is a supervised fusion stage where the module dynamically generates a set of 3D anchor boxes and query vectors，guided by a 3D bounding box loss function. This encourages the model to learn a geometrically consistent representation，improving localization accuracy via geometric constraints. Furthermore，a synchronous dual-view data augmentation strategy was designed； by applying consistent geometric transformations to both views，this strategy enriches sample diversity while maintaining their spatial correspondence，further improving model generalization in cluttered environments.

To validate the proposed method’s effectiveness，experiments are conducted using ResNet-50 as the backbone network. The study compares the model against two mainstream detection approaches： Transformer-based models （（denosing DETR （DN-DETR），real-time detection Transformer （RT-DETR）） and YOLO models （YOLO11-X）. Additionally，recent dual-view X-ray prohibited item detection methods YOLO_multi and Trans2Ray are included for comparison. All evaluations are performed on the DvXray dataset. Results show remarkable improvements in mAP₅₀ scores. For the overhead view （OL） view，the proposed method achieves improvements of 2.2%，12.2%，2.1%，7.6%，and 16.7% compared with YOLO11，DN-DETR，RT-DETR，Trans2Ray，and YOLO_multi，respectively. For the side view （SD） view，improvements are 5.7%，18.7%，6.8%，25.1%，and 17.0%，respectively. Analysis of individual category performance across 15 prohibited items reveals that the proposed method achieves superior detection in 12 categories for the OL view and 12 categories for the SD view. Under dual perspective joint statistical results，the method demonstrates optimal performance with an area under the P-R curve of 84.9% and a maximum F1 score of 83.5%. For visual analysis，detection results from three representative image sets were examined. The first set comprises contraband under challenging conditions characterized by cluttered backgrounds and rotation，the second set evaluates the model’s proficiency in detecting small objects，occluded backgrounds，and two contraband instances，and the final set consists of negative samples devoid of prohibited items. These visualizations demonstrate that our proposed model achieves optimal performance. Next，we visualized the attention mechanisms within the encoder. The corresponding visualizations for our proposed model were clearly superior to the baseline model. The experimental outcomes validate the effectiveness of the proposed methodology and its components through comprehensive ablation studies. These results demonstrate that the proposed dual-view detection framework effectively improves prohibited item detection performance in X-ray security screening applications.

This study proposes an improved detection network based on the RT-DETR architecture to enhance contraband detection accuracy in X-ray images，particularly in cluttered environments. The model incorporates a dual-view multiscale weighted fusion module with a multiscale window cross-attention mechanism，strengthening recognition capability for variably sized contraband. A 3D anchor-guided localization fusion module further imposes geometric constraints，providing explicit positional guidance to enhance the decoder’s localization accuracy. Additionally，our dual-view joint decision-making strategy enables comprehensive evaluation of detection performance across dual-view data. Experiments demonstrate the model’s competitive performance and robustness in dual-view X-ray contraband detection. However，the model faces persistent challenges including architectural complexity，excessive parameters，and time-consuming training，primarily from computational overhead induced by the dual-channel structure. Future work will explore lightweight dual-view fusion methods and develop efficient collaborative optimization algorithms to balance computational efficiency with detection performance.

Small-object detection in large-field-of-view （FOV） surveillance videos remains a persistent challenge in real-world applications such as construction-site safety monitoring，traffic supervision in highway service areas，and public-space management on university campuses. Compared with conventional close-range datasets，targets in large-FOV imagery are often extremely small and are heavily affected by background clutter，illumination variation，compression noise，motion blur，and frequent occlusion. Therefore，detectors trained on generic benchmarks typically face three intertwined bottlenecks： 1） scarce and costly annotations for tiny objects across diverse surveillance scenes； 2） weak and ambiguous visual features caused by limited pixel coverage and low signal-to-noise ratio，which makes tiny targets difficult to distinguish from the background； and 3） inaccurate localization，where even a deviation of a few pixels can substantially reduce intersection over union （IoU），thereby harming high-precision metrics and downstream tracking or analysis. To address these issues in a unified manner，this study proposes SOD-YOLO，a small-object-oriented detector designed for low-resolution，large-FOV surveillance videos. The primary goal is to enhance supervision signals，strengthen tiny-feature representation，and improve high-precision localization simultaneously，thereby delivering reliable performance under practical surveillance constraints.

SOD-YOLO follows a data-feature-localization co-design philosophy and introduces improvements in three aspects. 1） Virtual-real fusion sample generation. To alleviate the scarcity of real annotations and increase the diversity of tiny-object appearances and backgrounds，we propose a cost-effective data-generation strategy that combines SAM2 （segment anything model v2） semantic guidance with unreal engine （UE） virtual simulation. SAM2 is used to obtain fine-grained object masks and semantic cues，enabling high-quality object cutout，composition，and accurate label transfer for small objects. UE is used to generate controllable virtual surveillance scenes with precise ground-truth annotations，in which camera viewpoints，object-scale distributions，lighting conditions，and scene layouts are configured to mimic real surveillance patterns. These synthetic data are mixed with real samples to expand the training distribution，improving robustness to background variation and rare target configurations while keeping labeling costs manageable. 2） Tiny-object feature enhancement. Because tiny targets provide weak cues that are easily overwhelmed in large-FOV frames，we design a feature-enhancement mechanism with three components. First，video differential preprocessing explicitly highlights subtle foreground changes and suppresses irrelevant static textures. Second，strengthened multi-scale feature fusion better integrates fine-grained spatial detail from shallow layers with semantic context from deeper layers，allowing the network to preserve small-object signals while retaining discriminative context. Third，bilevel routing attention （BRA） is incorporated to allocate attention adaptively to informative regions and improve the model’s ability to perceive and distinguish tiny targets under clutter and occlusion. 3） Localization-oriented optimization. To address the strong localization sensitivity of small objects，SOD-YOLO adopts a decoupled head that separates classification and regression learning，reducing task conflict and stabilizing optimization when supervision is weak. In addition，we propose an α-CIoU loss to improve regression precision and encourage better alignment at higher IoU thresholds，directly targeting the common failure mode of loose or shifted bounding boxes for tiny objects. Collectively，these designs form an end-to-end detector that improves learning from limited data，enhances tiny-feature representation，and refines bounding-box regression.

Extensive experiments were conducted on three real-scene large-FOV surveillance datasets： construction site，highway service area，and university campus，covering multi-category detection of pedestrians，e-bikes，cars，trucks，and buses，as well as extremely small targets under complex backgrounds. Evaluation followed COCO-style metrics，with particular emphasis on high-IoU and small-object performance. On the construction-site dataset，SOD-YOLO achieved the best localization-sensitive performance，with an AP75 of 13.5% and an AP50s of 53.9%，indicating superior high-precision bounding-box regression and sensitivity to small targets in cluttered scenes. On the highway service-area dataset，which is characterized by dense traffic and large intra-class variation，SOD-YOLO reached the best overall AP of 42.6% and an AP75 of 29.5%，demonstrating robust general performance and high-IoU accuracy under complex multi-class conditions. On the especially challenging university-campus dataset，where the smallest objects may occupy only about 0.007 5% of image pixels and minor offsets severely degrade IoU，SOD-YOLO clearly outperformed the YOLOv7 baseline，improving AP，AP75，and AP50s by approximately 4.9%，2.5%，and 5.0%，respectively. To further verify generalization，we evaluated SOD-YOLO on the public VT-TOD visible benchmark under the official split. The model achieved leading performance，with a precision of 61.4%，an AP50：95 of 52.3%，an APs of 63.5%，and an APm of 58.2%，outperforming representative methods such as QueryDet，YOLOC，and Drone-YOLO. Ablation studies confirmed that every proposed component contributes positively： differential preprocessing produced the most notable gain by enhancing foreground saliency； multi-scale fusion and BRA provided additional gains by strengthening discriminative representations； and the decoupled head together with α-CIoU consistently improved high-IoU localization quality. Experiments on virtual-real mixing ratios showed that a balanced fusion strategy best exploits synthetic diversity while preserving realism，whereas overreliance on synthetic data can reduce generalization because of domain gaps.

This study proposes SOD-YOLO as an effective and reliable solution for small-object detection in low-resolution，large-FOV surveillance videos. By integrating virtual-real fusion data generation，tiny-feature enhancement，and localization-oriented optimization，SOD-YOLO improves both robustness and precision across diverse real surveillance scenarios and a public benchmark，particularly under high-IoU and small-object settings. The proposed framework provides a practical pathway toward deploying accurate small-object detectors in real monitoring systems where annotation scarcity，weak features，and localization sensitivity are critical constraints.

Object detection，a foundational task in computer vision，has attained remarkable performance under well-illuminated conditions with the advancement of deep learning techniques. However，its practical deployment is severely impeded by low-light environments，which are ubiquitous in real-world scenarios including nighttime surveillance，autonomous driving during dusk，and low-illumination indoor monitoring. Traditional object detection methods exhibit substantial performance degradation under low-light conditions，primarily stemming from three core challenges： excessive image noise induced by insufficient photon capture of imaging sensors，reduced contrast between foreground objects and backgrounds that obscures object contours，and severe loss of fine-grained details （e.g.，texture and edge information），which are all critical for accurate object localization and classification. Meanwhile，the development of low-light object detection techniques is further constrained by the scarcity of high-quality annotated low-light datasets. Annotating low-light images demands professional expertise to distinguish genuine object features from noise interference，resulting in high annotation costs and limited dataset scales. By contrast，large-scale natural image datasets （e.g.，COCO and ImageNet） with high-quality annotations are readily accessible，encompassing diverse object categories and scene variations. Consequently，this study aims to address the performance degradation of traditional object detection methods in low-light environments，alleviate the bottleneck posed by scarce high-quality annotated low-light images，and fully exploit the value of large-scale natural image datasets to provide efficient and reliable training support for low-light object detection.

An innovative low-light object detection framework，which adopts a core training paradigm based on normal-light pretrained models to maximize the utilization of existing high-quality natural image resources，is proposed to achieve the aforementioned objectives. The framework consists of two key modules designed to address domain differences and small/distant object detection challenges，respectively. First，to bridge the substantial domain gap between normal-light and low-light images，characterized by distinct illumination distributions and feature representations，an illumination-invariant feature alignment （IIFA） module is developed. This module implements cross-domain feature fusion through a twofold strategy： global illumination feature alignment and illumination-independent feature extraction. The global illumination alignment component normalizes the illumination statistics （e.g.，brightness and contrast） of normal-light and low-light features to a unified space，reducing the impact of illumination intensity differences. The illumination-independent feature extraction component employs an attention mechanism to focus on object structural features （e.g.，shape and contour） that are less affected by illumination changes，while suppressing illumination-related noise features. Additionally，an adaptive sample priority processing mechanism is embedded in the IIFA module to adjust the weight of training samples based on their domain adaptation difficulty dynamically. Samples with larger domain distribution differences （e.g.，low-light images with extreme noise） are assigned higher weights during training，enabling the model to prioritize learning from challenging samples and adapt more effectively to interdomain distribution differences. Second，aiming at the prominent challenge of detecting small or distant objects in low-light environments where degraded details and low contrast make it difficult for the model to capture discriminative features，a two-stage feature enhancement （TSFE） module is proposed. The first stage of the module performs multiscale feature fusion to aggregate low-level detail features and high-level semantic features of small/distant objects，compensating for the detail loss caused by low illumination. The second stage applies a local feature reinforcement attention mechanism to highlight the key feature regions of small/distant objects （e.g.，object heads and limbs for pedestrian detection） while suppressing background noise. This two-stage enhancement strategy ensures that the model can accurately locate and identify small or distant objects even in low-light conditions by sequentially enhancing feature completeness and discriminability.

Comprehensive experiments are conducted on the ExDark dataset，a widely recognized low-light object detection benchmark that contains 8 856 low-light images covering 12 common object categories （e.g.，person，car，and bicycle） and various low-light scenarios （e.g.，night streets and dim rooms）. The proposed model is compared with two mainstream low-light object detection methods： direct training on low-light datasets and normal-light pretraining followed by low-light fine-tuning. The evaluation metrics include mean average precision （mAP） for detection accuracy，precision （P） and recall （R） for category-specific performance，and frames per second （FPS） for detection efficiency. Experimental results demonstrate that the proposed model outperforms the comparison methods in all evaluation metrics，with particularly outstanding performance in small object detection. Specific metrics are as follows： In cross-domain adaptation scenarios （using normal-light pretrained models without fine-tuning on low-light data），the model achieves an mAP of 65.74% and an FPS of 57.03 frame/s，outperforming the direct low-light training method and maintaining comparable efficiency. When fine-tuned on the ExDark dataset，the proposed model achieves an mAP of 66.83%，a precision of 83.73%，and a recall of 54.78%，while maintaining an FPS of 48.81 frame/s，verifying the effectiveness of the IIFA and TSFE modules. The FPS of 48.81 also meets the real-time detection requirements of practical applications such as surveillance and autonomous driving.

This framework effectively bridges cross-domain differences between normal-light and low-light images，enhances the detection capability for small and distant objects，and remarkably improves overall low-light object detection performance，providing an efficient solution to low-light detection challenges.

Small-object detection in unmanned aerial vehicle （UAV） imagery remains one of the most challenging tasks in computer vision and is particularly important for surveillance，search and rescue，traffic monitoring，and precision agriculture. UAV images present several intrinsic difficulties： small targets often occupy fewer than 32 × 32 pixels in high-resolution images，exhibit low contrast against complex backgrounds，suffer from motion blur and varying illumination，and show extreme scale variation because of changing flight altitude. Traditional object detectors，which are primarily designed for ground-level imagery with relatively large objects，do not address these challenges well，because conventional convolutional neural networks progressively lose fine-grained spatial information through pooling and strided convolutions.

To overcome these limitations，we propose multi-scale frequency adaptive-you only look once（MSFA-YOLO），an innovative detector that integrates multi-scale feature extraction，frequency-domain analysis，and efficient feature enhancement. The architecture includes three key components. First，the multi-scale edge feature extractor module preserves and enhances edge information that is crucial for small-object detection. It processes input features through parallel pathways at multiple scales，applies the Sobel operator for edge extraction，and then fuses the resulting edge features with the original features through an adaptive cross-channel attention mechanism that dynamically adjusts their relative contributions. Second，the frequency-spatial adaptive module exploits the complementary nature of spatial and frequency-domain representations. It applies the fast Fourier transform to decompose spatial features into frequency components and uses a dual-branch design in which the spatial branch preserves location-specific information through depthwise separable convolutions，while the frequency branch applies learnable filters to selectively enhance relevant frequency bands. An adaptive gating mechanism then combines the two branches. Third，the lightweight feature fusion enhancement module addresses computational efficiency through a multi-branch structure with different kernel sizes and re-parameterizable convolutions，which are merged during inference without performance loss. Group convolution and channel shuffling are further introduced to reduce parameter count while maintaining representational capacity.

Comprehensive experiments were conducted on two challenging UAV datasets： VisDrone2019-DET，which contains 10 209 images with 54 200 annotated instances from ten object categories captured under diverse conditions，and UAVDT，which includes 80 000 frames with three vehicle categories and exhibits significant scale variation and occlusion. On VisDrone2019-DET，MSFA-YOLO achieved an mAP₅₀ of 38.1% with only 3.08 M parameters，representing a 4.3% improvement over the YOLO11n baseline （33.8%） and outperforming state-of-the-art lightweight methods including LWUAVDet （37.4% with 9.7 M parameters），Mamba-YOLO （36.4% with 5.66 M parameters），LUD-YOLOn （35.2% with 2.81 M parameters），and Hyper-YOLO （33.7% with 2.68 M parameters）. These results demonstrate a superior balance between efficiency and accuracy，with the highest detection accuracy among all compared methods while maintaining a compact model size. On UAVDT，MSFA-YOLO achieved an mAP₅₀ of 32.3% and an mAP_50-95 of 19.3%，surpassing the YOLO11n baseline （30.2% mAP₅₀ and 17.8% mAP_50-95） by 2.1% and 1.5%，respectively，while also outperforming Mamba-YOLO （31.7% mAP₅₀ with 5.66 M parameters），Hyper-YOLO （30.9% mAP₅₀），and YOLO12n （30.6% mAP₅₀）. Precision reached 45.1% and recall reached 31.2%，indicating robust detection performance across diverse scenarios. The proposed method achieves these results with only 3.08 M parameters，substantially fewer than Mamba-YOLO’s 5.66 M and comparable to the most efficient baseline models，validating the effectiveness of the overall design. Ablation studies confirmed the contribution of each component： the MEF module provided clear gains through edge enhancement，the FSA module improved representation through frequency-spatial fusion，and the LFFE module optimized the efficiency-accuracy trade-off while reducing computational overhead. Cross-dataset evaluation further demonstrated strong generalization，as models trained on VisDrone2019-DET achieved robust performance on UAVDT without fine-tuning，suggesting effective feature learning and domain adaptation.

This work presents MSFA-YOLO as a novel detection framework that addresses the core challenges of small-object detection in UAV imagery through the synergistic integration of multi-scale edge features，frequency-spatial adaptive processing，and efficient architectural design. The approach significantly improves performance over existing methods while maintaining the computational efficiency required for real-world UAV deployment. Its effectiveness stems from three main insights： preserving and enhancing edge information through multi-scale Sobel processing is crucial for targets with very limited pixel support； frequency-domain analysis provides complementary fine-grained cues that are often lost in purely spatial processing； and careful architecture design can maintain a compact model size without sacrificing detection accuracy. Future work will focus on improving robustness under extreme conditions such as low illumination and adverse weather，extending the framework to video-based detection with temporal consistency constraints，exploring self-supervised pre-training for domain adaptation，and deploying the system on edge devices with hardware-specific optimizations. Overall，the proposed method represents a meaningful step toward practical，deployable UAV-based detection systems for challenging real-world environments.

Road damage detection is a fundamental component of the intelligent maintenance and management of transportation infrastructure. As global transportation systems continue to modernize and road networks expand rapidly，pavements inevitably deteriorate because of a range of external and internal factors. Traditional inspection methods，including manual surveys and existing automated approaches，still face major challenges in scalability，efficiency，and accuracy，which limits their suitability for large-scale deployment. There is therefore an urgent need for a high-performance，accurate，and robust framework for road damage detection and maintenance. In practical scenarios，road-surface damage often exhibits elongated geometries with high aspect ratios，low texture，low contrast，and strong heterogeneity. These characteristics pose fundamental challenges to existing algorithms，which tend to focus on local texture or semantic cues while overlooking the intrinsic physical properties of road damage. In particular，such models fail to capture the directional feature coupling inherent in the continuous spatial distribution of cracks and the edge attenuation of blurred potholes. The lack of direction-aware features and precise positional cues weakens long-range dependency modeling and reduces responsiveness to targets with indistinct boundaries. In addition，the hierarchical convolution and downsampling operations in convolutional neural networks （CNNs） progressively degrade cross-scale features，causing the loss of critical detail from high-resolution shallow feature maps.

To address these limitations，this study proposes a novel multi-stage spatial-aware detection network （MSANet） built on the YOLOv11 framework. The design follows a progressive feature enhancement-fusion-optimization strategy tailored to the physical characteristics of elongated cracks and blurred-edge potholes. In the direction-sensitive feature enhancement stage，a hybrid Manhattan self-attention and coordinate attention （MCA） mechanism is introduced. Manhattan self-attention models the spatial continuity of crack axes，while coordinate attention improves the localization of blurred-edge defects. Together，these components create a dual-driven enhancement scheme that strengthens both directionality and detail representation，enabling the model to capture long-range spatial dependencies more precisely. To mitigate feature degradation caused by repeated downsampling，a cross-scale feature concat （CFC） module is incorporated into the neck. By cascading feature maps across resolutions，the CFC module balances semantic completeness at larger scales with detail richness at smaller scales，thereby compensating for the loss of fine-grained information. Finally，in the detection head，a coordinate-aware convolution kernel （C3K2_CAC） is embedded within the C3K2 block. This module jointly optimizes spatial position and channel response through direction-aware spatial attention and dynamic channel reweighting，producing anisotropic representation enhancement in high-dimensional feature space. While maintaining computational efficiency，it significantly improves edge clarity，detection robustness，and global discriminative capability. During training，experiments were conducted on a standardized platform using Ubuntu 20.04 and PyTorch. YOLOv11s served as the baseline for comparative and ablation studies. Across all experiments，the same hyperparameters were used： a batch size of 32，a learning rate decayed from 0.01 to 0.000 1，an image resolution of 640 × 640，stochastic gradient descent （SGD） as the optimizer，and a training schedule of 300 epochs. All other settings followed the default network configuration to ensure fairness and reproducibility.

Experiments were conducted on the publicly available RDD2022 Chinese subset，which contains multiple road-damage categories，including longitudinal cracks （D10），transverse cracks （D10），alligator cracks （D20），potholes （D40），and repaired regions. Performance was evaluated using seven standard metrics： precision （P），recall （R），mAP@0.5，mAP@0.5：0.95，F1-score，frames per second （FPS），and GFLOPs. The results show that MSANet significantly outperforms existing state-of-the-art detectors. Compared with mainstream models，MSANet achieves an mAP@0.5 of 0.877 and a comprehensive performance score of 0.845 （Recall： 0.811，Precision： 0.883，F1-score： 0.845），surpassing YOLOv8s （0.824） and RT-DETR （0.801）. Its mAP@0.5：0.95 improves by 4.8% over the baseline，confirming the robustness of the proposed design. In fine-grained damage detection，MSANet further demonstrates strong adaptability，achieving a 4.15% increase in mAP@0.5 （to 0.878），a 3.18% increase in mAP@0.5：0.95 （to 0.649），and a 2.69% gain in F1-score （to 0.840）. Importantly，the model maintains a high inference speed of 142 frame/s，representing only a 6% reduction from the baseline while still substantially outperforming real-time detectors such as RT-DETR. This balance between accuracy and efficiency supports practical deployment in real engineering settings，where rapid decision-making and reliable predictions are both essential. Ablation studies further validate the contribution of each proposed module. The MCA module improves performance without significant computational overhead. The CFC module effectively fuses low-level detail with high-level semantics across network stages，introducing cross-scale contextual information that yields stable gains of 0.5% in mAP@0.5 and 2.3% in mAP@0.5：0.95. The C3K2_CAC module introduces only a modest increase in computational cost while enhancing the spatial awareness of neck features and supplying the detection head with more discriminative representations. Detection heatmaps further confirm that MSANet provides better focus on target regions，stronger suppression of background noise and irrelevant activations，and more complete coverage of damage areas. To assess generalization and deployment potential，experiments were also performed on additional public datasets and field-collected road images，where MSANet achieved consistently promising results，demonstrating strong robustness and scalability across diverse environments.

Efficient and accurate road damage detection is crucial for the intelligent maintenance of transportation infrastructure. By combining spatial-awareness enhancement with detail preservation，the proposed MSANet framework addresses long-standing challenges in directional modeling and fine-grained feature retention. The model achieves substantial improvements in detection accuracy while preserving real-time inference capability，offering both theoretical novelty and engineering practicality. MSANet therefore represents a promising solution for intelligent road infrastructure management and provides useful insights for the design of next-generation detection frameworks that seek to balance efficiency，accuracy，and scalability. With its strong adaptability to both benchmark datasets and real-world imagery，MSANet also lays a foundation for future integration into large-scale intelligent transportation systems and smart-city applications. The code for this paper will be available at https://www.scidb.cn/s/RZBnIz.

Active learning for object detection commonly estimates sampling uncertainty using detector confidence and localization deviation，which struggles to identify high-confidence yet semantically wrong false positives. Meanwhile，diversity sampling typically relies on global visual descriptors at the image level，making it difficult to capture semantic structures at the category/instance level and often leading to redundant samples. To address these issues，we introduce cross-modal priors so that the selection signals can be informed not only by the detector’s visual evidence but also by language-aligned semantics. The goal is to improve the reliability （robustness against semantic mismatch） and effectiveness （utility per labeled image） of active sample selection under constrained annotation budgets.

We propose semantic enhanced active learning （SEAL），a cross-modal prior-driven active learning framework that leverages the contrastive language-image pretraining （CLIP） vision-language model to enhance uncertainty and diversity criteria without any additional supervision. In uncertainty sampling，for each image，SEAL extracts CLIP features on candidate detection boxes and compares the detector’s predicted category with CLIP’s category evidence. We design a visual-semantic consistency score that jointly reflects the detector’s confidence，its localization quality，and the agreement between visual predictions and cross-modal semantics. Instances with low consistency are prioritized，yielding a more robust instance-level uncertainty measure that explicitly penalizes high-confidence semantic mismatches，which is precisely the failure mode that frustrates conventional uncertainty heuristics in diversity sampling. To reduce redundancy while preserving category coverage，SEAL aggregates CLIP embeddings of all objects within an image and builds a category-level structural representation. We then compute inter-image structural similarity in this semantic space and perform category-aligned diversity selection，encouraging batches that simultaneously cover rare or under-represented classes and span heterogeneous intraclass appearances. This process mitigates the tendency of global-feature methods to overselect visually similar images that contribute limited new information.

We conduct comprehensive experiments on MS COCO and Pascal VOC under multiple baselines and annotation budgets. Across settings，SEAL consistently outperforms strong active learning methods，yielding higher detection accuracy per labeled image. Representative results include the following： RetinaNet on Pascal VOC （20% labeled data），mAP@0.5 = 72.4%，exceeding the strongest prior method by 0.8%； RetinaNet on MS COCO （10% labeled data），AP@（0.50：0.95） = 23.9%，an improvement of 0.5% over the best baseline under the same budget； Faster R-CNN on MS COCO （40% labeled data），AP@（0.50：0.95） = 33.3%，surpassing the best competing approach by 0.22%. These gains are attributable to two complementary effects. First，the consistency-aware uncertainty ranking correctly downweights detections that are visually confident but semantically unreliable，thereby allocating labels to genuinely informative instances. Second，the category-aligned diversity explicitly controls semantic coverage and inter-image variation，reducing selection redundancy and ensuring that annotation efforts expand the breadth （more classes covered） and depth （richer intraclass variation） of the training set. In practice，we observe that SEAL improves data efficiency： for a fixed target accuracy，fewer labels are required relative to visual-only strategies，and for a fixed budget，the achieved AP is higher. The approach remains effective across different detectors and budgets，indicating good robustness and transferability. Beyond headline metrics，qualitative analyses show that SEAL preferentially selects images containing semantically ambiguous contexts （e.g.，objects with occlusion，atypical viewpoints，or confusing backgrounds）. This selection behavior accelerates learning in regions of the data distribution where detectors typically underperform. Because CLIP embodies large-scale language-vision alignment，the semantic signal complements the detector’s purely visual evidence and provides a principled handle on semantic correctness，which traditional confidence/localization surrogates cannot fully capture.

SEAL demonstrates that cross-modal priors can be harnessed to make active learning for object detection more reliable and more cost effective. By combining a visual-semantic consistency measure for uncertainty with a category-aligned structural criterion for diversity，SEAL systematically reduces semantic mismatch errors during selection and curbs redundancy，leading to higher AP under the same labeling budget and，equivalently，lower labeling cost to reach a target accuracy. Overall，our results on COCO and VOC indicate that infusing active selection with language-aligned semantics is a practical and effective path toward scalable，label-efficient detection.

In space life science research，drosophila melanogaster （fruit fly） serves as a representative model organism for exploring the effects of microgravity environments on biological behavior，neural activity，and metabolism. Achieving precise and efficient multi-object tracking of drosophila is crucial to understanding their motion patterns and behavioral mechanisms. However，given their small body size，nonlinear motion，and high visual similarity among individuals，especially in dense scenes，existing multi-object tracking （MOT） methods face considerable challenges. To address this，we constructed a drosophila multi-object tracking dataset under ground-based experimental conditions，designed for space station applications. The dataset contains high-density video sequences with complex and dynamic behavior，providing valuable data support for intelligent behavior analysis of drosophila. In terms of methodology，existing MOTR-based approaches achieve end-to-end multi-object tracking but suffer from inherent limitations that lead to suboptimal detection performance. To overcome this issue，this study proposes a novel end-to-end multi-object tracking framework that effectively enhances detection accuracy and tracking robustness.

A ground-based simulated space environment dataset for multi-object tracking of drosophila was constructed on the basis of SpaceAnimal，comprising 20 video sequences （a total of 2 500 annotated frames） that capture high-density and high-dynamic motion characteristics. Moreover，we propose DN-MeMOTR，attempting to enhance the performance of detection by reconstructing ground truths with noises. Noised samples are generated from ground truth data by flipping the label and shifting the bounding box. We use label noise and box noise scales to control the scale of noise separately. Noised samples and initial learnable detect queries serve as detect queries and are concatenated with track queries，sequentially updated via self-attention and cross-attention. Furthermore，noised samples are employed solely during training and play no role in the inference stage. During inference，joint-filtering module is implemented to identify and remove targets that exhibit identical or similar characteristics across detect queries and track queries. Specifically，the ReID head outputs ReID features of detect and track queries. First，we filter out detect outputs whose scores are lower than the detection score threshold to obtain high score detection outputs indexes，then calculate the ReID similarity matrix and GIoU distance between detection outputs and track outputs. Two matrixes are linearly combined through a weighted sum.

We conduct experiments and analysis on drosophila dataset and compare the results with those of other prevalent MOT methods，such as ByteTrack，OC-SORT，and MeMOTR. MeMOTR demonstrates suboptimal detection performance compared with other detection-based tracking frameworks，which can be attributed to its inherent limitations. Our approach achieves the highest HOTA score of 71.8%，surpassing MeMOTR and Hybrid-SORT by 4.9% and 9.0%，respectively. With respect to the MOTA metric，our DN-MeMOTR obtains 87.6%，representing a substantial improvement of 19.3% over MeMOTR and 2.9% over Hybrid-SORT. We also obtain the highest DetA score of 73.4% and the highest IDF1 score of 84.9%. To enable a more direct comparison between DN-MeMOTR and the original MeMOTR，we conduct inference on video clips. According to the visualization results，MeMOTR tends to miss some targets，whereas DN-MeMOTR demonstrates better performance. The ablation study proves the effectiveness of denoising training and joint-filtering module.

We construct a multi-object tracking dataset of drosophila to simulate and support behavior analysis tasks under future space microgravity scenarios by extending the drosophila dataset of SpaceAnimal and propose DN-MeMOTR，which incorporates denoising training and joint-filtering modules. Denoising training aims to improve the network’s detection performance，overcoming the drawback of MOTR-like models. By contrast，ReID features and the joint-filtering module attempt to alleviate mismatching，boosting the performance of the original MeMOTR. Experiments show that our approach achieves superior performance on the drosophila dataset. The ablation study demonstrates the effectiveness of our components. It lays a solid foundation for the in-depth analysis of drosophila motion patterns and behavioral characteristics.

With the growing adoption of deep learning in infrared object detection，such detectors have become core technical support for key applications including nighttime warning in autonomous driving，nocturnal security monitoring，and long-range body-temperature screening in epidemic prevention. Their detection accuracy and resistance to interference are therefore closely tied to operational safety and system reliability. Infrared object detectors are widely used because they are not limited by visible-light conditions and can penetrate certain partial occlusions. Nevertheless，like other deep-learning-based vision systems，they remain vulnerable to security threats. In particular，the emergence of adversarial examples has posed serious challenges to the robustness and security of infrared object-detection models. As an important tool for evaluating and improving model security，research on adversarial examples is therefore of great significance. Existing work on infrared adversarial examples mainly falls into two categories： digital adversarial attacks and physical adversarial attacks. Although digital attacks can achieve high success rates，the perturbations exist only in the digital domain and cannot be directly transferred to the physical world. Current research on physical attacks is concentrated mainly in the visible-light domain； for infrared imagery，the unique imaging mechanism limits high-level semantic cues such as texture and color，which makes physical adversarial optimization substantially more difficult. Although existing studies have explored this area actively，white-box methods remain the most effective attack setting because they can exploit model structure and parameter information. However，current approaches still suffer from two major limitations. The first is imbalance in multi-objective optimization： most methods use fixed weights to balance attack effectiveness and physical realizability，and when the optimization difficulty of achieving strong adversarial effects greatly exceeds that of maintaining physical feasibility，the process can easily lead to insufficient attack strength or poor real-world applicability. The second is inadequate physical constraint design： existing methods typically consider only basic constraints such as patch size and shape，while neglecting pixel-level consistency in the digital domain，which often causes the resulting patches to fail under real infrared imaging conditions.

We propose a dynamic objective prioritization-driven optimization method for infrared adversarial patches. First，we introduce two regularizers： aggregation regularization and sparsity-binarization regularization. Aggregation regularization uses a graph-theoretic approximation of the local clustering coefficient （LCC） to force mask pixels into a compact and continuous geometry，while sparsity-binarization regularization drives mask values toward a bimodal distribution with sharp boundaries，thereby facilitating material cutting and fabrication. Next，to avoid the optimization imbalance caused by fixed loss weights，we devise a dynamic objective prioritization strategy. Key performance indicators （KPIs） are defined to quantify the current optimization difficulty of the attack loss，aggregation loss，and binarization loss. Focal loss （FL） is then used to reallocate computational resources： difficult objectives receive larger gradients，whereas easier ones are down-weighted，ensuring that the solver focuses on the most challenging constraints at each stage. Finally，we design a dual-projection optimization algorithm that combines projected gradient descent （PGD），tailored for adversarial tasks，with a momentum-based weighted update. The first projection constrains the updated mask to the feasible set immediately after each gradient step，preventing wasted gradients； the second projection recalibrates the mask after momentum fusion，smoothing local gradient noise and promoting global convergence under strict physical constraints.

Experiments were conducted primarily on the FLIR ADAS v1.3 dataset，with additional validation on FLIR ADAS v2.0 and LLVIP. YOLOv3 was used as the target detector，and attack effectiveness was evaluated in both digital and physical settings. In the digital domain，the average attack success rate （ASR） reached 75.08%，and the average precision （AP） of YOLOv3 across datasets dropped to 36.18%，significantly outperforming state-of-the-art methods. Moreover，the proposed method achieved the best ASR and AP across datasets，demonstrating strong generalization. To verify cross-model robustness，we further tested the method on detectors with different architectures，including single-stage models （YOLOv5 and YOLOv8），a two-stage detector （Faster R-CNN），and the Transformer-based DETR. Except for DETR，the ASR exceeded 74% on all models，and the ASR against Faster R-CNN reached 78.90%，confirming strong transferability across detector architectures. In physical experiments，heat-insulating gel was used as the thermal-insulation material. The position and shape of the adversarial mask were optimized digitally，and the material was then cut into physical patches for testing in both indoor controlled environments and outdoor scenes. Indoors，the ASR reached 98.5% in the frontal view and remained above 84.5% at ±30° under changes in angle，distance，and posture. Notably，even a single patch maintained a high success rate at left and right viewing angles of 30°. In complex outdoor environments，the ASR still reached 90.5%，indicating that the proposed method can retain high attack effectiveness under diverse physical interference using only a single patch.

By integrating dynamic objective prioritization with a dual-projection optimizer，the proposed infrared physical adversarial patch method resolves the core conflict between attack aggressiveness and physical realizability. The dynamic strategy balances multiple objectives，the dual-projection optimizer ensures strict constraint satisfaction and stable convergence，and the dual-regularization design reduces fabrication difficulty. The method achieves strong digital attack effectiveness and cross-model robustness while maintaining stable performance in complex physical scenarios，thereby striking an effective balance between attack strength and deployment cost. It provides a reliable red-team tool for security evaluation of infrared detection systems and offers important insights for the design of countermeasures，especially for safeguarding infrared perception modules in autonomous driving and security applications.

Medical image anomaly detection represents a fundamental challenge in clinical diagnostics，aiming to automatically identify and localize potential lesion regions without manual annotations. Current reconstruction-based anomaly detection methods predominantly rely on features extracted from models pretrained on natural image datasets such as ImageNet. While these approaches have demonstrated promising results，they face considerable limitations due to the substantial domain shifts between natural and medical images. These domain discrepancies lead to semantic misalignments during feature reconstruction，resulting in two primary issues： oversensitivity to normal anatomical variations and insufficient discriminative capability for detecting subtle or early-stage lesions. Such limitations substantially hinder the clinical applicability of existing methods，particularly in screening scenarios where early detection is crucial. To address these challenges and to overcome the dilemma where fine-tuning pretrained encoders often leads to pattern collapse while keeping them frozen limits domain adaptation，this study proposes a cross-domain self-supervised representation learning （CSRL） framework，specifically designed to enhance both the accuracy and robustness of unsupervised medical image anomaly detection through effective domain adaptation and multi-scale feature optimization.

The proposed CSRL framework operates through two carefully designed sequential learning stages，each targeting distinct aspects of the anomaly detection pipeline while maintaining architectural coherence. In the first stage，a domain-adaptive contrastive learning （DACL） network is constructed on the basis of the bootstrap your own latent architecture. This module employs an innovative dual-path online-target network framework to facilitate robust cross-domain transfer of pretrained encoders. The online and target networks process different strongly augmented views of the same medical image through random cropping，color jittering，and Gaussian blurring operations. The target network parameters are updated via an exponential moving average of the online network，with a momentum coefficient of 0.99 to ensure training stability. A symmetric negative cosine similarity loss function is employed to maximize feature consistency between the two views，effectively driving the encoder to learn augmentation-invariant and domain-adaptive representations. This sophisticated learning mechanism enables the model to capture essential medical image characteristics while discarding domain-specific artifacts from natural images，thereby establishing a solid foundation for subsequent anomaly detection tasks. In the second stage，we construct an end-to-end feature reconstruction network specifically optimized for anomaly scoring and precise localization. The encoder component is initialized with the adapted weights from the DACL module and remains completely frozen during this training phase to preserve the learned domain-invariant representations. The network extracts multi-scale deep feature maps at three different resolutions，which are subsequently processed by a specially designed bottleneck layer comprising three residual blocks with skip connections for effective feature fusion. A symmetric decoder architecture with transposed convolutional layers then reconstructs the features at corresponding scales，maintaining spatial integrity throughout the reconstruction process. To enhance the model’s focus on potential lesion regions further，we introduce a synergistic attention enhancement （SAE） module that innovatively integrates three complementary attention branches： coordinate attention for precise spatial localization，channel attention for feature recalibration，and spatial attention for regional emphasis. These attention mechanisms are dynamically aggregated through learnable weighting coefficients to refine feature responses in an adaptive manner. Additionally，a multi-scale anomaly fusion （MAF） module computes cosine distance-based anomaly maps at different scales and employs learnable weights combined with spatial attention to generate the final optimized anomaly map. The entire framework is optimized using a joint loss function that combines reconstruction error，fusion consistency，and sparsity constraints，ensuring comprehensive training supervision across multiple objectives.

Comprehensive experiments were conducted on three publicly available medical image datasets covering diverse modalities，i.e.，Asia Pacific Tele-Ophthalmology Society（APTOS），International Skin Imaging Collaboration（ISIC），and BR35H，to evaluate the proposed method rigorously under various clinical scenarios. The CSRL framework was compared with 10 state-of-the-art methods，including autoencoder-based models （AE，VAE），generative approaches （GANomaly，f-AnoGAN），memory-based models （PaDiM），and recent pretraining-based methods （STFPM，MKD，EDC，EA2D）. All experiments were performed using consistent evaluation protocols and five independent runs to ensure statistical significance. Quantitative results demonstrate that our method achieves optimal performance in image-level anomaly detection tasks. On the BR35H dataset，CSRL achieved an AUC of 99.90%； on the ISIC dataset，an AUC of 91.79%； and on the APTOS dataset，an AUC of 97.71%. Compared with the current state-of-the-art method，the best AUC of our method is improved by 0.35%，3.52%，and 0.02% on the APTOS，ISIC，and BR35H datasets，respectively. Extended ablation studies provided deeper insights into the individual contributions of each module. The DACL module alone improved AUC from 88.23% to 91.72% on the ISIC dataset，highlighting its crucial role in domain adaptation. The SAE module contributed considerably to precise localization，while the MAF module effectively integrated multi-scale information for comprehensive anomaly detection. When combined，the full model achieved 94.74% AUC in its best run，demonstrating the synergistic effect of all components. Qualitative visualization of anomaly localization maps revealed that CSRL produces highly concentrated and clinically interpretable heatmaps that align well with actual lesion contours，outperforming comparison methods in spatial consistency and anatomical plausibility. The generated activation maps show precise correspondence with pathological regions while maintaining minimal responses in healthy tissues，substantially improving clinical interpretability for medical practitioners.

This study presents a novel CSRL framework that effectively addresses the fundamental challenge of semantic discrepancy in transferring pretrained models to medical image anomaly detection. By systematically integrating domain-adaptive contrastive learning，synergistic attention enhancement，and intelligent multi-scale anomaly fusion，the framework achieves remarkable improvements in both discriminative capability and spatial precision of anomaly detection. Extensive experimental validation across multiple medical imaging modalities confirms the effectiveness and generalizability of the proposed approach，establishing new state-of-the-art performance while maintaining clinical interpretability. The framework provides a reliable and scalable solution for unsupervised anomaly detection in medical images，with immediate potential applications in early diagnosis and clinical screening programs. Future work will focus on three key directions： developing more efficient cross-domain adaptation strategies for few-shot and limited-data scenarios，optimizing model complexity and inference speed for practical clinical deployment，and enhancing sensitivity to subtle anomalies through advanced feature alignment mechanisms. These research directions aim to strengthen both the theoretical foundations and practical applicability of self-supervised learning in medical imaging，ultimately contributing to improved healthcare outcomes through more accessible and accurate diagnostic tools.

Low-dose computed tomography （LDCT） has become an important imaging modality for reducing patient radiation exposure in diagnostic and screening protocols. However，the inherent trade-off between radiation dose and image fidelity introduces severe quantum noise and streak artifacts，which degrade anatomical visibility and reduce diagnostic confidence. Although deep-learning-based reconstruction methods，especially convolutional neural networks （CNNs） and generative adversarial networks （GANs），have made remarkable progress in learning direct mappings from LDCT to normal-dose CT （NDCT），they still suffer from several intrinsic limitations. These include a tendency toward excessive smoothing that erodes fine pathological detail，sensitivity to distribution shifts between training and clinical data，high computational complexity，and a static processing paradigm that cannot adapt to the spatially heterogeneous noise characteristics typical of LDCT scans. To address these challenges，this paper introduces HRDRL-Net （high-resolution deep reinforcement learning network），a novel framework that reformulates LDCT reconstruction as an adaptive，pixel-level decision-making process. The goal is to achieve an optimal，context-aware balance between aggressive noise suppression and faithful preservation of diagnostically important structures.

HRDRL-Net is grounded in the theory of Markov decision processes （MDPs） and implemented using the asynchronous advantage actor-critic （A3C） algorithm. Its core innovation is a dual-path，multi-branch cooperative architecture. The upper path contains a dedicated low-dose noise suppression module （LDNM） with four branches—dilated convolution，identity mapping，detail enhancement，and noise suppression—combined with a dual-attention mechanism that includes both channel and spatial attention for multi-scale semantic feature extraction and refinement. The lower path uses a predefined Sobel operator followed by convolutional layers to explicitly extract and enhance structural edge information. Features from the two paths are fused adaptively through a gating mechanism，producing a rich state representation for the agent. A comprehensive action space of 14 predefined image-processing operators is further designed，including Gaussian filters of different strengths，edge-preserving bilateral filters，median filtering，non-local means filtering，guided filtering，and adaptive sharpening. This allows the agent to select the most appropriate local processing strategy for each region. Training is guided by a composite reward function with three components： 1） a pixel-level fidelity term that minimizes mean squared error，2） a gradient-structure similarity term that enforces edge and texture consistency，and 3） a local variance penalty term that selectively suppresses noise in homogeneous regions without over-smoothing high-gradient anatomical details. Both the policy network （actor） and the value network （critic） are implemented as fully convolutional networks，enabling efficient per-pixel action-probability prediction and state-value estimation. The entire system is trained end to end： the agent iteratively interacts with the LDCT image environment，receives rewards based on its reconstruction actions，and updates its policy to maximize cumulative future quality gains.

HRDRL-Net was rigorously evaluated on two publicly available benchmark datasets，the Mayo Clinic LDCT Challenge Dataset and the Piglet Dataset. It was compared with five state-of-the-art deep learning methods： residual encoder-decoder convolutional neural network （RED-CNN），dual domain generative adversarial network，DU-GAN），CT-former，self-adaptive weight embedded lightweight network （SWELNet），and multi-stage multi-order context aggregation （MMCA）. Quantitative results on the Mayo testset show that HRDRL-Net achieved a peak signal-to-noise ratio （PSNR） of 32.319 ± 1.016 dB，a structural similarity index measure （SSIM） of 0.897 ± 0.020，and a gradient magnitude similarity deviation （GMSD） of 0.063 ± 0.008，outperforming all competing methods. Compared with the baselines，this corresponds to an average improvement of ～1.3% in PSNR，～0.7% in SSIM，and a ～6% reduction in GMSD. On the Piglet dataset，the model achieved a PSNR of 32.263 ± 1.158 dB and an SSIM of 0.900 ± 0.009，confirming strong cross-domain generalization. Statistical significance was verified by Friedman tests （p < 0.001 for all metrics on both datasets） followed by post hoc Nemenyi tests. Ablation studies further validated each core design component： the composite reward improved PSNR by ~0.7 dB compared with a pixel-only reward； the full action set yielded an approximately 1.2% SSIM gain over a basic action set； the dual-path architecture reduced GMSD by ～2.3% relative to single-path variants； and the complete LDNM improved PSNR by 0.8 dB over a simple dilated-convolution baseline. Visual analysis，supported by residual maps and edge-intensity profiles，shows that HRDRL-Net achieves thorough noise removal in homogeneous regions while preserving fine vasculature，organ boundaries，and tissue textures，whereas competing methods tend to leave residual noise，blur structures，or distort anatomy. In addition，HRDRL-Net remains computationally practical，requiring only 0.3 GB of GPU memory and offering competitive inference speed，thereby balancing reconstruction quality and resource efficiency.

This work presents HRDRL-Net as a deep reinforcement learning framework that addresses the fundamental tension between noise suppression and detail preservation in LDCT reconstruction. By formulating reconstruction as a sequential，adaptive decision-making problem，HRDRL-Net moves beyond static processing pipelines. Its combination of a dual-path feature extractor，a rich action space，and a composite reward function enables intelligent pixel-level adaptation to local image content. Extensive experiments demonstrate its superiority over current methods in quantitative metrics，visual quality，statistical significance，and generalization，while maintaining practical efficiency. The framework therefore provides a robust，high-fidelity，and clinically promising solution，and it points to a new direction for intelligent，adaptive processing in medical imaging.