In intelligent traffic monitoring systems, small target vehicle detection in complex scenes faces challenges such as low feature resolution, severe occlusion interference, computational redundancy, and insufficient bounding-box regression accuracy. To balance detection accuracy with deployment efficiency on edge devices, an improved YOLOv8 framework based on dynamic sparse attention and a lightweight dual-branch structure was proposed. The method first introduced a bidirectional routing sparse attention mechanism (ReBiAttention) that enhanced the retention of shallow features for small targets by dynamically filtering key features through a two-level routing strategy. Subsequently, GSConv and VoV-GSCSP modules were integrated to reduce computational cost while dynamically adjusting multi-scale feature weights. An improved DynamicHead was applied for multi-task adaptive optimization, and a modified ShapeIoU loss function with shape- and scale-aware weighting was employed to improve localization accuracy. Experiments on the UA-DETRAC dataset showed that, relative to baseline YOLOv8n, Precision, Recall, and mAP@0.5 increased by 8.739%, 1.685%, and 7.225%, respectively, while the parameter count decreased by 4.3%. This method provided an efficient solution for accurate detection of small-target vehicles in complex traffic scenarios.

Aeroengine blades are core components of engines; tiny surface defects can lead to serious safety accidents. Traditional vision detection technology is limited by motion blur, low dynamic range, background redundancy, and so forth. To address these challenges, a method of aeroengine blade defect detection based on Dynamic Vision Sensor (DVS) was proposed. Dynamic vision sensor produced data in an asynchronous event-stream format, and were therefore referred to as event camera, which exhibited the advantages of large dynamic range, high frame rate, and strong ability to capture small targets. Firstly, a defect detection platform based on DVS was built, and its imaging characteristics and advantages were explored. On this basis, the first Event-based Defect Detection Dataset of Aeroengine Blade (EDD-AB) dataset based on DVS was constructed, covering nearly 6 000 images of scratches, point marks and edge damage, with approximately 12 000 finely annotated target labels. The dataset was released as open source (link: https://github. com/NiBieZhouMei5520/EDD-AB.git). Furthermore, a multi-scale defect-detection algorithm based on asynchronous event-stream frame aggregation (AEAF-ABDD) was proposed: event streams were visualized through frame aggregation technology using a fixed time window; a Multi-Resolution Adaptive Feature Pyramid Network (MRAFPN) was developed to enhance multi-scale defect feature extraction capability; a lightweight SimAM attention mechanism was incorporated to strengthen focus on key regions; a star-convolution module (StarNet) was fused to improve the efficiency of high-dimensional nonlinear feature mapping, enabling accurate detection of multi-scale defects on complex curved workpieces. Experiments demonstrated that AEAF-ABDD achieved a mean Average Precision (mAP) of 97.7% on the EDD-AB dataset and a detection speed of 105 frames per second, substantially outperforming mainstream algorithms. An efficient solution for automated quality inspection of highly reflective curved workpieces was thereby provided, promoting the application of DVS in the field of industrial inspection.

With the widespread application of Building Information Modeling (BIM) technology in the field of engineering design, the Revit platform-driven 3D forward design has developed into a relatively mature solution for superstructure. However, as a critical structural component, isolated foundations still face challenges such as low efficiency and poor information integrity in 3D modeling and drawing generation. Moreover, due to the closed data interfaces of mainstream structural analysis software, it is extremely challenging to directly extract reinforcement information from fundamental database files and synchronize to the properties of Revit elements. To address this limitation, a new parametric modeling algorithm for isolated foundations based on AutoCAD layer and text recognition was proposed, utilizing the Revit Application Programming Interface (API) and the Model-View-ViewModel (MVVM) design pattern. By importing foundation layout and reinforcement annotation drawings generated by structural analysis software (e.g., YJK) into Revit, the algorithm calculated the center coordinates and planar dimensions of foundations using layer-recognition methods. Then, a matching logic was established between foundations and reinforcement annotations according to their relative positions. Subsequently, text recognition was employed to extract the corresponding annotations’ numbering, height, and reinforcement information, which were then automatically written into the family properties of the model. Additionally, the extraction of model attributes and geometric face references enabled automatic annotation of foundation dimensions and reinforcement. Finally, the proposed method was applied to the parametric modeling and drafting of isolated foundations for a self- built cold storage factory and was compared against conventional methods. The results demonstrated that this algorithm significantly improved the efficiency of three-dimensional modeling and annotation drawing for isolated foundations, while exhibiting excellent compatibility with calculation files exported from PKPM software.

As large models continue to grow in scale, the memory footprint and computational overhead of model inference have become critical challenges. Mixed-precision quantization is an effective approach to reduce resource consumption, but existing methods suffer from insufficient outlier handling, significant quantization accuracy loss, and inefficient memory access. To address these issues, a memory-aligned mixed-precision quantization method for large models was proposed. First, weights were divided into SIMD-aligned groups, and outlier groups were identified via group-wise significance analysis, with high-significance groups quantized to 8 bit and others to 2 bit. A block-wise compensation strategy was introduced to mitigate accuracy degradation caused by 2 bit quantization. Furthermore, an efficient packing and storage scheme was designed for mixed-precision weights, where a bitmap was used to record the bit width of each data block, enabling random access. Experimental results demonstrated that the proposed method significantly reduced memory usage and improved computational efficiency while maintaining model accuracy. Specifically, on Llama2-7 B/13 B/70 B, the approach achieved perplexity reductions of 8.13/2.84/1.37 on WikiText-2 and 5.80 on C4 relative to state-of-the-art baselines. The quantized 70 B model reduced weight storage by approximately 87% compared with BF16. Across seven QA benchmarks, an average accuracy gain of 6.24% was achieved. Last, these results indicated that a mixed-precision quantization method for large language models via memory alignment could simultaneously improve compression ratio, memory-access efficiency, and overall model performance.

The detection of Focal Liver Lesions (FLLs) is crucial for disease diagnosis and treatment. Traditional detection methods face many challenges, and the application of deep-learning technology brings new opportunities. In view of this, this paper systematically reviewed the deep-learning-based FLLs detection methods, and provided specific research directions for the development of FLLs detection technology by analyzing the advantages and disadvantages of related technologies. First, the public datasets of liver radiological images were organized and summarized, and the key role of data preprocessing in improving model performance was expounded. Secondly, the 2D and 3D detection algorithms based on convolutional neural networks, Transformer, knowledge distillation, and other technologies were compared and analyzed, revealing the technical evolution path from local feature modeling to global spatio-temporal correlation. In addition, the temporal feature fusion methods for multi-phase images were examined in depth, providing new ideas for dynamic lesion characterization. The review showed that existing methods had achieved breakthroughs in detection accuracy and efficiency, but still faced challenges such as insufficient sensitivity to small lesions, weak cross-device generalization, and lack of clinical verification. Future research was recommended to accelerate the clinical transformation and application of deep learning in auxiliary diagnosis of liver lesions through multi-center data collaboration, lightweight algorithm design, and enhanced interpretability.

The organizational management of construction sites is a critical aspect in engineering management; however, traditional human supervision method is constrained by many environment limitations and low efficiency. In recent years, multiple government departments have issued relevant policies advocating deep integration of artificial intelligence with the real economy to promote high-quality and efficient economic development. The accuracy, efficiency, and automation advantages of Computer Vision (CV) technology have gradually led to its widespread application in the field of construction supervision. Meanwhile, the drones, which can efficiently obtain complex and varied visual data of construction scene, demonstrate their application potential in CV-based construction supervision tasks. However, the current researches on drone-based construction scene detection are limited, and the lack of overhead-perspective construction-scene image datasets restricts further development in the field. Therefore, the DJI Mavic 3T drone was utilized to obtain construction-site images to establish an open-source overhead image dataset for construction scene UB-CSD. Several advanced object-detection algorithms were selected for comparative experiments on the UB-CSD dataset, and the reasons for performance differences were analyzed from multiple dimensions such as model workflow design, computation principle, and task characteristics. The mAPs of every algorithm’s detection result were YOLOv8 and YOLOv10 (96.1%), YOLOv9 (96.0%), YOLO11 (95.7%), DETR (95.3%), Faster-RCNN (76.3%) and RetinaNet (72.1%). The analysis results indicated that the YOLO series algorithm constituted the most optical algorithm for drone-based object detection tasks in construction scenes. By establishing a new open-source special dataset and conducting comparative experiments, the conclusion drawn provided effective data and experimental cases to support future safety production management and object-detection algorithm research in the construction industry.

To address the significant decline in matching accuracy and the number of correspondences exhibited by existing image-matching methods under large viewpoint variations, an improved image-matching approach based on E-LoFTR was proposed. Firstly, based on a strategy of viewpoint rectification followed by fine-grained matching, a novel two-stage SIFT-based viewpoint-rectification module was proposed, which leveraged the viewpoint invariance of the Scale-Invariant Feature Transform (SIFT) algorithm and the geometric alignment capability of homography to enhance matching accuracy under large viewpoint variations. Then, a directional-gated attention mechanism was designed that employed a cascaded structure of multi-directional convolutions and dynamic gating to extract queries (Q), keys (K), and values (V). The injected geometric priors significantly enhanced the model’s robustness. Lastly, to mitigate information loss during the upsampling of fused features, the Fusion-DySample module was incorporated to further improve performance. Experimental results on the public MegaDepth dataset showed that our method achieved relative pose estimation AUCs of 57.1%, 72.7%, and 83.9% under rotation error thresholds of 5°, 10°, and 20°, respectively, outperforming E-LoFTR by 0.7%, 0.5%, and 0.4%. On the newly constructed NewMega dataset based on MegaDepth and on a private industrial dataset, our method also demonstrated substantial improvements in both the number of matches and matching accuracy.

The traditional development approach for complex forming equipment typically relies on Document-Based Systems Engineering (DBSE), which often leads to issues such as protracted development cycles due to inadequate requirement analysis, incomplete requirement coverage caused by textual ambiguity, and equipment development delays lagging behind technological iterations. These shortcomings frequently result in final designs that fail to meet target performance metrics and require inefficient, repetitive modifications. Therefore, in the conceptual design stage of complex forming equipment, and drawing on the U.S. Department of Defense Architecture Framework (DoDAF) combined with Model-Based Systems Engineering (MBSE), an MBSE-based conceptual-design method for complex forming equipment was proposed. This method utilized five viewpoints, including panoramic viewpoint, capability viewpoint, operational viewpoint, systems viewpoint, and standards viewpoint, as entry points for the conceptual design of complex forming equipment. Through multi-perspective analysis, the method performed top-level requirements acquisition, requirements refinement analysis, functional analysis, and system modeling across four design levels. Eleven types of models were established using the Systems Modeling Language (SysML), enabling digital and procedural expression in the conceptual design stage of complex forming equipment. Finally, superplastic-forming equipment was used as a representative example to demonstrate the application of this design method. The application of the method addressed the shortcomings of traditional design approaches and demonstrated that the method provided effective guidance for the forward development of complex forming equipment.

Existing research on multi-view stereo scheme utilizes depth-estimation algorithms to achieve stereo representation by establishing a mapping relationship between the physical and digital worlds. Supervised learning-based neural networks have achieved accurate and high-fidelity 3D reconstruction results through training. However, in-the-wild visual reconstruction remains challenging due to the lack of rendered depth priors and wide-baseline characteristics of images. A novel system was proposed to obtain optimized depth for naturally collected multi-view images without prior information by applying an unsupervised learning network and semantically optimized Neural Radiation Field (NeRF) rendering. First, preliminary depth information for wild multi-view images were produced without ground truth based on unsupervised deep learning. Subsequently, in a separate NeRF module, a diffusion model was used to construct a surface semantic rendering loss, enabling a fine-grained volumetric representation. Experimental results on the benchmark dataset validated the performance of the proposed system by improving an average of 24.6% of the overall metrics, compared with other state-of-the-art schemes. A novel wild wide-baseline dataset was also applied to verify the generalization performance, and the proposed system reduced the reconstruction error by up to 40.8% compared with all methods.

Traditional 3D point-cloud classification methods tend to suffer from insufficient generalization and catastrophic forgetting in Few-Shot Class-incremental Learning (FSCIL) scenarios. The pretrained vision-language model CLIP (Contrastive Language-Image Pre-training), which contains rich 2D shape priors, has been shown to effectively enhance 3D FSCIL performance. However, existing CLIP-based frameworks still lack flexibility and adaptability in multimodal feature extraction and fusion, which limits classification accuracy during incremental stages. To address these shortcomings, a 3D FSCIL approach with deeply fused multimodal features was proposed. An adaptive adapter based on gated units and residual blocks was introduced to achieve multi-scale feature alignment and redundancy suppression, and a multimodal global feature dynamic fusion module with self-attention was designed to adaptively adjust the weight allocation of different feature streams according to sample characteristics, thereby obtaining more consistent and complementary fused representations. Specifically, point clouds were rendered into multi-view depth maps, and features were extracted using both the original CLIP visual encoder and a CLIP encoder pretrained on depth maps, combined with point-cloud geometric features. After processing through the adaptive adapter, these features were fed into the attention-based fusion module and aligned with semantic features extracted by the CLIP text encoder for classification. In addition, contrastive learning loss, multi-view and geometric perturbation-based data augmentation strategies, and a memory-replay mechanism were incorporated to effectively mitigate overfitting and forgetting under few-shot conditions. Experiments on ShapeNet, ModelNet, and CO3D demonstrated that the proposed method consistently achieved higher accuracy across incremental stages compared with existing 3D FSCIL approaches, while significantly reducing both relative accuracy drop rates and maximum stage fluctuations.