With the advancement of image processing and artificial intelligence， deep learning-based algorithms have become increasingly important in the tasks of image target detection and recognition. In the aerospace domain， satellite remote sensing object detection consistently confronts challenges， including cluttered imaging backgrounds， numerous minuscule targets， and wide dynamic imaging ranges. In recent years， convolutional neural network-based approaches have witnessed significant progress in satellite remote sensing object detection， particularly in fine-grained target recognition. These advancements play crucial roles across domains such as military reconnaissance， postdisaster reconstruction， and resource exploration. Given the challenges of large coverage， small and dense targets， and complex imaging backgrounds in satellite-based remote sensing images， large and complex neural networks have been utilized to represent image features for further target detection. Although large neural networks exhibit certain detection capabilities， they are difficult to deploy in space-based remote sensing tasks because of the high real-time requirements and limited computing resources. To address these issues， this study proposes a lightweight space-based remote sensing image target detection algorithm that integrates multiattention mechanisms in the spatial domain and channels. It deploys remote sensing image data processing and target detection algorithms to a remote sensing edge intelligent computing platform， achieving efficient and accurate target recognition and analysis for remote sensing images. This approach provides a solution for future in-orbit fast target detection algorithm processing and real-time tracking of detection targets.

Based on a You Only Look Once version 11 model （i.e.， YOLOv11n）， the proposed algorithm integrates the channel prior convolutional attention （CPCA） mechanism， which combines channel and spatial attention mechanisms. It utilizes the channel attention mechanism to generate a channel attention map. Subsequently， this map is multiplied element-wise with the model’s input feature map to produce a channel-weighted feature map. This channel-weighted feature map is then fed into a depthwise convolution module to generate a spatial attention feature map. The CPCA mechanism can dynamically allocate attention weights across channel and spatial dimensions， enriching the network’s target features by extracting channel-wise and spatial attention features， thereby enhancing the network’s feature extraction capability. By employing a 2D convolutional layer based on partial convolution （Pconv）， which convolves only a subset of input channels， it leverages redundant compression in interchannel feature maps. This approach avoids the issue of excessive parameters typically introduced by adding attention modules. Consequently， the improved model reduces the parameter count by 0.48 M （approximately 18.53%） compared with the original YOLOv11n. This approach partially addresses the challenge of deploying network models on embedded devices. For ensuring consistent dimensions between the two branches of Pconv， a max-pooling operation is applied to the nonconvolved channels， downsizing the feature maps to half their original dimensions. Through leveraging pointwise convolution to fully utilize the representational capacity of channel-wise features， this design reduces the computational load while preventing significant degradation in the model’s feature extraction capability.

During validation on the DIOR dataset， the proposed algorithm was compared with various YOLO algorithms for object detection. Experimental results demonstrate that real-time detection transformer（RTDETR） has the largest parameter count at 9.42 M， YOLOv11n has 2.59 M parameters， and YOLOv11n_CBAM has 2.74 M. By contrast， the proposed model contains only 2.11 M parameters， accounting for 81.47% of those of the original YOLOv11n. Meanwhile， compared with the original YOLOv11n algorithm， the proposed method achieves a mean improvement of 1.9% in accuracy and 1.2% in recall. The neural network processing unit （NPU） inference latency of YOLOv11n is 19.6 ms， whereas the proposed algorithm achieves only 14.8 ms. This result indicates a reduction of 4.8 ms in comparison with the original model， representing a 24.49% speed improvement. Additionally， the NPU-deployed YOLOv11n model attains an accuracy of 0.799 and a recall of 0.642， whereas the proposed algorithm achieves 0.819 accuracy and 0.652 recall. Accordingly， no potential accuracy degradation occurs during model migration and deployment. Compared with merely adding the CPCA module， the proposed algorithm exhibits a slight accuracy decrease of 0.10% but reduces the parameter count by 0.66 M. When contrasted with solely incorporating the Pconv module， it shows a marginal parameter increase of 0.08 M， yet it improves the accuracy by 1.7%.

Targeting space-based remote sensing minute object detection tasks， this study draws inspiration from the YOLOv11n model to propose a lightweight object detection algorithm that integrates multiattention mechanisms in the spatial domain and channels and contextual information. This approach significantly enhances detection accuracy while effectively reducing model parameters. By refining the attention mechanism in YOLOv11n， we introduce an improved architecture incorporating the CPCA module. This architecture enables comprehensive feature extraction for minute objects across spatial and channel dimensions， effectively mitigating missed detections and false alarms in spaceborne imagery. The conventional 2D convolutional layers in YOLO are replaced with Pconv-based designs， circumventing parameter inflation typically caused by attention modules. This replacement achieves an 18.53% parameter reduction and model lightweighting. Finally， through NPU-optimized deployment， the model’s hardware compatibility is enhanced. Compared with the original YOLOv11n， the proposed algorithm reduces inference time by 4.8 ms while maintaining detection accuracy， meeting real-time monitoring requirements. The solution proves exceptionally resource efficient for space-based engineering deployment with constrained computational resources and memory， providing crucial technical support for onboard implementation in spaceborne remote sensing systems.

Most existing vision-based rail defect detection methods face challenges such as high parameter counts， computational complexity， slow detection speeds， and limited accuracy. Aiming to overcome these limitations， this paper introduces a lightweight pyramid cross-attention network （LPCANet） for orbital image defect detection using RGB images and depth images.

LPCANet adopts MobileNetv2 as its backbone network to extract multiscale feature maps from RGB images. Simultaneously， a lightweight pyramid module （LPM） is employed to extract similarly-sized feature maps from depth images. Each stage of the LPM comprises a sequence of operations including max pooling， a 3 × 3 convolutional layer， batch normalization， and ReLU activation， enabling efficient extraction of features from depth images. By leveraging deep learning， RGB-D technology， and salient object detection， LPCANet efficiently extracts multiscale feature representations from RGB and depth data. The LPM handles depth image features， while the backbone captures detailed pyramid features from RGB images. Subsequently， a cross-attention mechanism （CAM） is applied to integrate the feature maps from both modalities， enhancing the network’s focus on relevant defect regions. Additionally， a spatial feature extractor （SFE） is introduced to further boost defect detection performance. Finally， a “pixel shuffle” operation is used to restore the output to the original image resolution.

The proposed scheme was computationally evaluated using the PyTorch library in an environment equipped with an NVIDIA 3090 GPU， alongside several benchmark models for comparison. For the evaluation of LPCANet， three publicly available unsupervised RGB-D rail datasets were used： NEU-RSDDS-AUG， RSDD-TYPE1， and RSDD-TYPE2. Experimental results on the NEU-RSDDS-AUG dataset indicate that LPCANet achieves excellent efficiency， with 9.90 million parameters， a computational complexity of 2.50 G， a model size of 37.95 MB， and a running speed of 162.60 frames per second. Compared to 18 existing rail defect detection schemes， LPCANet exhibits superior lightness in performance. In particular， when compared against CSEPNet， the current best-performing model， LPCANet achieves improvements across several evaluation metrics： +1.48% in S_{\alpha }Sα， +0.86% in intersection over union （IOU）， +0.14% in F_{\beta }^{\mathrm{m}\mathrm{a}\mathrm{x}}Fβmax， +0.03% in mean average precision （mAP）， and +1.77% in mean absolute error （MAE）. An ablation study was conducted on four upsampling methods （interpolation， transposed convolution， patch merging， and “pixel shuffle”） to evaluate their effectiveness within the LPCANet framework. Among these， the “pixel shuffle” method demonstrated clear advantages and was found to be the most suitable for the LPCANet model. Further ablation studies were conducted on four different components （backbone network， LPM， SFE， and CAM）. The results indicate that CAM and SFE notably enhance the detection performance of LPCANet. An in-depth analysis of various backbone networks confirmed that LPCANet model is not only compatible with existing backbone networks but also consistently achieves superior detection results. Aiming to evaluate the model’s generalization capability beyond rail datasets， experiments were also conducted on three non-rail defect datasets： DAGM2007， MT， and Kolektor-SDD2. The results show that LPCANet delivers improved performance across three key metrics： mAP， MAE， and IOU， demonstrating its potential for general-purpose defect detection tasks.

The LPCANet model proposed in this study effectively combines the advantages of traditional and deep learning approaches， demonstrating strong practical value in the field of rail defect image processing. In the future， this scheme will focus on further reducing the model size to achieve rapid detection speeds while ensuring further improvements in performance quality.

Weakly supervised semantic segmentation （WSSS） aims to reduce the cost associated with annotating “strong” pixel-level labels by using “weak” labels， such as points， bounding boxes， image-level class labels， and scribbles. Among these， image-level class labels are the most cost-effective and readily available； however， leveraging them for precise segmentation remains a considerable challenge. A widely used WSSS approach based on image-level class labels generally comprises the following steps： 1） training a neural network for image classification using the class labels； 2） using the trained network to generate class activation maps （CAMs）， which serve as seed regions for the segmentation task； and 3） refining these CAMs into pseudo-labels， which are then used as the ground truth to supervise a segmentation network. These steps can be integrated into a single collaborative stage； typically， single-stage frameworks are highly efficient due to their simplified training pipeline. However， the quality of pseudo-labels is crucial to the overall performance of semantic segmentation. High-quality pseudo-labels result in superior segmentation outcomes， whereas noisy or inaccurate pseudo-labels hinder the capability of the model to learn meaningful features. WSSS based on image-level labels faces considerable challenges due to the absence of precise positional and shape-related information， making it difficult to generate accurate segmentation maps. These challenges have led to the development of various approaches， which can be broadly categorized into two types： single-stage methods and multistage methods. Although single-stage methods offer greater efficiency and simplify the overall training process， they often produce less accurate pseudo-labels. This condition is due to the limited refinement of CAMs， resulting in imprecise supervision signals that ultimately degrade segmentation performance. Aiming to alleviate these limitations， a simple yet novel single-stage WSSS framework that incorporates knowledge distillation is introduced to enhance pseudo-label quality without relying on any additional external supervision. The framework enhances the feature learning process within the teacher-student network using a dual-stage knowledge distillation module. This module allows the student network to acquire more dynamic and informative knowledge from the teacher network while preserving key features， thereby enhancing the overall robustness of the student model. Moreover， to further improve segmentation accuracy， a pseudo-label correction module based on a Gaussian mixture model （GMM） is introduced. This module refines the pseudo-labels by modeling the distribution of the CAMs， resulting in highly accurate and reliable supervision signals. The combination of dual-stage knowledge distillation and the Gaussian correction module ensures accurate learning and improved segmentation results， even under weak supervision signals such as image-level labels. Ultimately， the proposed method effectively mitigates the impact of noise during training and enhances the accuracy of the generated pseudo-labels， resulting in superior semantic segmentation outcomes in WSSS tasks.

A novel weakly-supervised semantic segmentation method， aimed at addressing the challenges posed by noisy data points and weak supervision， is proposed. First， a dual-stage knowledge interaction module is introduced to enhance the feature learning process of the teacher and student networks. By enabling highly effective knowledge exchange between the two networks， the proposed approach notably reduces the impact of noise during training， leading to robust feature extraction. Additionally， a Gaussian correction module is proposed to enhance the quality of pseudo-labels. This module refines the pseudo-labels by modeling the distribution of class activation maps. By fitting the distribution more accurately， the module corrects potential errors in the pseudo-labels， ensuring that the model learns from high-quality， refined labels. Therefore， the method boosts the overall performance of weakly-supervised semantic segmentation， making it more robust to noise and improving segmentation accuracy. This method provides a promising solution for weakly-supervised segmentation tasks.

The mIoU values of this method on the PASCAL VOC 2012 and MS COCO 2014 datasets were 74.8% and 42.3%， respectively， surpassing other comparative methods. Specifically， on the PASCAL VOC 2012 dataset， the proposed method achieved a 3.7% improvement over ToCo， an 8.8% enhancement compared to AFA， a 7.5% increase relative to TSCD， and 1.1% compared to BECO. On the MS COCO 2014 dataset， the method improved performance by 2.2% compared to TSCD， 3.4% compared to AFA， and 5.3% compared to AuxSegNet+. Additionally， the mIoU values of different categories are compared on the PASCAL VOC 2012 validation set. The experimental results showed that the method outperformed the competing methods in 16 categories. Notably， for the background class， the method achieved an mIoU of 92.4%， the highest among all methods evaluated. This result indicates that the method effectively leverages the Gaussian correction module to reduce misclassification of background regions， thereby improving segmentation performance. Furthermore， the method achieved notable improvements in categories such as bird， bottle， car， chair， and cow， further demonstrating its effectiveness.

The proposed method effectively mitigates the impact of noise during training and address the issue of incomplete pseudo-label generation through the integration of a dual-stage knowledge distillation module and a Gaussian correction module. This approach achieves remarkable performance improvements compared to existing methods. Overall， the results demonstrate notable advantages in end-to-end weakly supervised semantic segmentation and holds considerable research value.

In recent years， rapid advancements in digital technology have positioned digital orthodontics as a critical research focus within the field of dentistry. Among the numerous challenges encountered during orthodontic treatment， designing an accurate dental arch line is fundamental for precisely calculating the target positions of teeth after treatment. The dental arch line should not only follow the natural growth patterns of the teeth but also satisfy aesthetic and functional requirements essential for optimal orthodontic outcomes. However， current automated tooth alignment methods typically model the dental arch line using Beta functions， which are inherently limited by their restricted degrees of freedom. This limitation often prevents these methods from generating curves that accurately capture the ideal dental arch form， especially when dealing with complex or irregular tooth arrangements. Moreover， orthodontists frequently require customized dental arch lines tailored to each patient’s unique oral condition. However， arch lines fitted solely from the patient’s initial intraoral scan may not always align with therapeutic or aesthetic expectations， necessitating labor-intensive manual adjustments. These challenges highlight the need for a flexible and precise approach to dental arch line design that effectively meets clinical standards and patient-specific requirements. Aiming to address these limitations， this paper proposes a novel dental arch line fitting method based on cumulative chord length parameterization combined with Hermite interpolation. This approach aims to enhance control over the dental arch shape， improve fitting accuracy， and provide orthodontists with a highly effective and efficient tool for designing and adjusting dental arch lines during orthodontic treatment planning.

The proposed method begins by inputting the patient’s intraoral scan data， which undergoes a series of preprocessing steps to ensure data quality and consistency. A tooth segmentation algorithm is then applied to accurately isolate each individual tooth， following internationally recognized dental segmentation standards. After segmentation， a landmark detection algorithm is employed to extract key landmarks from each tooth， capturing essential geometric and morphological features. These landmarks serve as the foundation for subsequent dental arch line fitting. Aiming to facilitate the interpolation process， the extracted landmarks are initially reparameterized using cumulative chord length parameterization. This process generates a naturally distributed set of interpolation points along the dental arch by accounting for the varying distances between adjacent landmarks， thereby preserving the true spatial relationships among teeth. Subsequently， Hermite interpolation is employed to construct the dental arch line through the parameterized points. By incorporating position and tangent information， Hermite interpolation enables the construction of smooth， continuous curves with enhanced local control. Aiming to ensure fitting accuracy and smoothness， a coefficient matrix is constructed to formulate a system of linear equations. Solving this system yields the final dental arch line， represented as a piecewise continuous function. This piecewise structure allows for precise local adjustments， making the method particularly effectively for accommodating complicated or irregular tooth arrangements. Furthermore， this paper introduces two new mathematical evaluation metrics： the mean shortest distance and the maximum shortest distance between the extracted landmarks and the fitted curve. These metrics offer an objective and robust means of assessing how accurately the generated dental arch line conforms to the patient’s actual dental morphology.

The proposed fitting method， which integrates cumulative chord length parameterization with Hermite interpolation， exhibits substantial improvements over traditional approaches in dental arch line fitting. First， compared to conventional Beta function-based methods， the proposed approach offers substantially greater flexibility by allowing the inclusion of additional control points. This increased degree of freedom directly addresses the limitations of Beta functions， particularly their inability to support localized shape modifications. The resulting dental arch line provides orthodontists with the flexibility to manually adjust specific， predefined control points， enabling localized adjustments tailored to individual patient needs. The proposed method excels in offering excellent controllability for global and local morphology adjustments of the dental arch line while maintaining high accuracy and smoothness across all regions， attributed to the use of its piecewise functional structure. Experimental evaluations further highlight the advantages of the proposed method. Qualitative analyses show that the generated curves more naturally align with actual dental arch shapes than those produced by conventional methods. Quantitative results， assessed using the proposed shortest distance-based evaluation metrics， confirm a notable improvement in fitting accuracy and alignment with natural tooth arrangements. Additionally， the proposed method enhances clinical flexibility， allowing orthodontists to efficiently adjust the dental arch line by manipulating a limited number of control points， minimizing the need for extensive manual corrections. In practical scenarios， the proposed fitting method is integrated into an existing automated tooth alignment system. This integration led to noticeably improved orthodontic outcomes， further validating the practical effectiveness and clinical applicability of the proposed method.

Compared to existing dental arch fitting methods， the proposed method based on cumulative chord length parameterization and Hermite interpolation demonstrates clear advantages in fitting accuracy and flexibility. This method effectively addresses key limitations of traditional approaches， such as difficulty in achieving an ideal dental arch line and limited adaptability to patient-specific variations. By notably increasing the degrees of freedom and enhancing the controllability of the fitting function， the method produces dental arch lines that are not only smooth and accurate but also highly customizable to meet the diverse clinical requirements of modern orthodontic practice. Furthermore， the introduction of quantitative evaluation metrics offers a systematic and objective framework for assessing fitting quality， ensuring that the resulting dental arch lines are aesthetically aligned and functionally sound. Beyond its technical advantages， the method also improves clinical efficiency by reducing the time and effort typically required for dental arch adjustments during treatment planning. Overall， the proposed method offers strong technical support for the advancement of digital orthodontics and holds substantial potential for broader clinical adoption. This paper establishes a solid foundation for further innovations in automated orthodontic treatment systems， opening new possibilities for personalized and precise dental care.

With the rapid advancement of 3D sensing technologies such as LiDAR （light detection and ranging） and depth cameras， large-scale 3D point clouds have emerged as a crucial data source for a wide range of applications， including autonomous driving， robotic navigation， augmented reality， and urban scene reconstruction. Compared to 2D images， point clouds offer precise spatial geometry and provide a comprehensive representation of the environment without perspective distortion. Additionally， they are robust to variations in lighting and texture. Point cloud segmentation plays a crucial role in scene analysis and interpretation. The segmentation can be categorized into three types： semantic segmentation， instance segmentation， and joint semantic-instance segmentation. Semantic segmentation partitions a 3D scene into informative regions and assigns each region to a specific class. Instance segmentation identifies and separates individual objects at the point level， including those that belong to the same semantic category. In recent years， researchers have increasingly focused on combining the two tasks to achieve more consistent and informative scene-level interpretations. Joint semantic-instance segmentation leverages the intrinsic correlation between semantic and instance-level segmentation， enabling the two tasks to complement and reinforce each other. In 3D point cloud contexts， this joint approach substantially improves the capability of the system to comprehend complex environments and offers strong technical support for the development of intelligent systems. Consequently， this approach has become an area of growing interest and active research. However， most existing methods for joint semantic-instance segmentation rely on simplistic feature fusion strategies， which limit their effectiveness in fully capturing the potential relationship between semantic and instance features. Aiming to address this limitation， an enhanced attention-based joint semantic-instance segmentation network is proposed. This network is designed to effectively model and utilize the correlation between semantic and instance information.

The enhanced attention-based joint semantic-instance segmentation neural network （EAJS-Net） incorporates a semantic feature extraction module based on an attention mechanism. This module focuses on the local neighborhood of each point and dynamically adjusts attention weights to emphasize key information， thereby enhancing the extraction of semantic features across points. Additionally， an attention-enhanced semantic/instance feature fusion module is introduced， which adaptively learns the similarity between central and adjacent features. This design reinforces key characteristics and effectively captures the correlation between instance and semantic segmentation， ultimately improving overall segmentation accuracy. EAJS-Net integrates PointNet++ and PointConv as its backbone network and comprises three main components： a point feature enhancement module， an encoder-decoder module， and an enhanced attention-based joint segmentation module. The input to EAJS-Net includes N × 9 dimensional point cloud data， where N represents the number of points， and the nine dimensions include coordinate values （XYZ）， color information （RGB）， and normalized coordinates. A semantic feature extraction module based on an attention mechanism is employed to effectively capture local contextual information between points. The enhanced features extracted by this module are then fed into the encoding layer， which includes four encoding modules： one attention pooling-based set abstraction layer adapted from PointNet++ and three feature encoding layers derived from PointConv. The corresponding decoding layer comprises four decoding modules： three deep feature decoding layers derived from PointConv and one feature propagation layer from PointNet++. By utilizing the attention pooling-based set abstraction layer from PointNet++， the network effectively captures spatial geometric relationships among features. Through the combination of the encoding and decoding layers， the initial semantic and instance features of the point cloud are extracted， laying the foundation for accurate joint segmentation. An enhanced attention module is designed to adaptively learn the similarity between central and neighboring features through dual attention mechanisms， which dynamically compute attention weights. These dual attention weights are summed and applied to the initial semantic features， resulting in enhanced semantic representations. This module is embedded within the semantic branch of the joint segmentation module， enabling more effective integration of semantic and instance features to improve joint segmentation accuracy. The encoded features are then upsampled through two parallel decoder branches to generate an instance feature matrix and a semantic feature matrix， which serve as inputs to the joint segmentation module. Within this module， the semantic and instance branches are integrated using the enhanced attention mechanism. The final output comprises instance embeddings and semantic predictions， supporting precise and consistent segmentation results.

The proposed network is evaluated on the Stanford large-scale 3D indoor spaces （S3DIS） dataset and ScanNet V2 to assess its performance on point cloud segmentation tasks. Six fold cross-validation is performed on the S3DIS dataset， and the results of EAJS-Net are compared with those of the state-of-the-art （SOTA） methods. For semantic segmentation on the S3DIS dataset， EAJS-Net achieves a mean intersection over union （mIoU） of 65.9%， overall accuracy （oAcc） of 89.1%， and mean accuracy （mAcc） of 76.0%. Compared to JSNet++， these results represent improvements of 3.5% （mIoU）， 0.4% （oAcc）， and 3.2% （mAcc）. For instance segmentation， EAJS-Net reaches a weighted coverage rate of 61.1%， outperforming JSNet++ by 4.1% （mean weighted coverage， mWCov）， 4.6% （mean coverage， mCov）， and 1.2% （mean recall， mRec）. On the ScanNet dataset， EAJS-Net improves the mIoU for semantic segmentation by 3.2% and increases the weighted coverage rate for instance segmentation by 2.8% compared to JSNet. Visual comparisons between EAJS-Net and other SOTA methods are also presented， demonstrating that EAJS-Net consistently achieves superior segmentation results， even in complex indoor scenes. In addition， ablation experiments are conducted to validate the effectiveness of individual modules within the network. The enhanced attention-based joint segmentation module in EAJS-Net dynamically adjusts attention weights to effectively capture various features， successfully integrating semantic and instance features into the semantic feature space. This integration notably enhances the performance of the semantic segmentation task.

Aiming to address the limitations of existing feature fusion strategies that fail to fully capture inter-instance semantic correlations， this paper proposes a novel semantic-instance joint segmentation network， EAJS-Net， based on an enhanced attention mechanism. A new semantic feature extraction module is designed to capture contextual relationships among points. Additionally， an enhanced attention module is introduced to effectively aggregate instance features into the semantic feature space. This improved feature fusion strategy boosts the performance of joint semantic-instance segmentation. Experimental results demonstrate that EAJS-Net effectively integrates semantic and instance features， substantially improving the accuracy of both segmentation tasks compared to SOTA methods.

RGB-D salient object detection （SOD） combines complementary information from RGB and depth images， offering substantially enhanced performance in complex and challenging scenes compared to RGB-only models. This technique has gained considerable attention in the academic community due to its capability to effectively capture salient objects by leveraging visual and spatial information. However， existing RGB-D detection models face several key challenges. First， efficiently utilizing and fusing multi-modal information from RGB and depth inputs remains a difficult task due to the inherent differences between the two modalities. RGB images provide rich color and texture details but lack depth information， whereas depth maps offer spatial cues but are often noisy or of low quality. Second， achieving accurate boundary detection is particularly challenging in cluttered or noisy environments. Noisy depth maps and cluttered backgrounds can obscure object contours， making it difficult to predict sharp and precise boundaries. These challenges highlight the urgent need for a robust model that can effectively integrate RGB and depth information while simultaneously addressing noise and enhancing boundary precision.

Aiming to address these challenges， a novel method， the cross-modal feature fusion and detail-enhanced RGB-D salient object detection network （CFADNet）， is introduced. The proposed network incorporates two innovative modules： the cross-modal attention fusion enhancement module （CAFEM） and the boundary feature extraction module （BFEM）. The CAFEM is designed to enhance the integration of RGB and depth features by leveraging attention mechanisms that emphasize the most informative aspects of each modality. Specifically， channel attention is applied to the RGB features to suppress noise and enhance critical color and texture details. Similarly， spatial attention is applied to the depth features to emphasize spatial regions that are relevant for salient object detection. This attention-based fusion mechanism ensures that the model effectively retains global semantic information from the depth map while preserving fine-grained details from the RGB image. The fusion process is structured in multiple layers， progressively integrating features at different scales to fully utilize the complementary strengths of RGB and depth modalities. In contrast， the BFEM is specifically designed to improve the accuracy of salient object boundaries. Accurate contour detection is crucial for generating high-quality saliency maps； thus， BFEM leverages low-level CNN features， which are rich in edge and texture information. These features are refined through channel attention， which filters out noise and irrelevant details， enhancing the clarity of boundary-related cues. The refined features are then used to guide cross-modal feature decoding， ensuring that the final saliency maps exhibit sharp and accurate boundaries. By combining the edge-extraction capabilities of low-level CNN features with the semantic richness of cross-modal features， BFEM notably improves boundary precision in RGB-D salient object detection.

Aiming to evaluate the performance of CFADNet， extensive experiments are conducted on four widely used RGB-D salient object detection datasets： NJU2K， NLPR， STERE， and SIP. These datasets encompass a wide range of diverse and challenging scenes， making them ideal for evaluating the generalization capability of the proposed model. CFADNet is compared against 16 state-of-the-art RGB-D salient object detection methods， including DCF， CIRNet， and CAVER， using standard quantitative metrics such as mean absolute error （MAE）， F-measure（F_β）， and structural similarity （S_α）. CFADNet demonstrated superior performance across all datasets， particularly excelling in the MAE metric. Specifically， this network outperformed the second-best method by 6.9%， 10.5%， 9.7%， and 2.4% on the NJU2K， NLPR， STERE， and SIP datasets， respectively. These substantial improvements highlight the effectiveness of the attention-based fusion strategy and edge refinement mechanisms. Furthermore， CFADNet consistently achieved higher F-measure and Sα scores， indicating that the model not only reduces pixel-level errors but also more accurately preserves the overall structure and shape of salient objects compared to competing methods. In addition to quantitative evaluations， qualitative comparisons are conducted to visually assess the performance of CFADNet in various challenging scenarios. Results show that the proposed method generates saliency maps with sharp and accurate boundaries， even in cases where salient objects exhibit complex edges or are embedded in cluttered and noisy backgrounds. This finding demonstrates the robustness of CFADNet in handling difficult scenes by effectively separating salient objects from their background while preserving fine boundary details. The visual results further confirm that CFADNet successfully captures global semantic information and local detail， ensuring accurate identification and clear isolation of salient objects from the background.

This paper presents CFADNet， a cross-modal feature fusion and detail-enhancement network for RGB-D SOD， designed to address the two major challenges： effective multimodal feature fusion and accurate boundary detection. CFADNet introduces two novel modules， the CAFEM and the BFEM. CFADNet effectively integrates RGB and depth information while notably enhancing the precision of salient object boundaries. The attention mechanisms used in the CAF0EM enable the network to fully leverage the complementary information from RGB and depth modalities. Simultaneously， the BFEM module focuses on refining edge details， resulting in sharper and more accurate saliency predictions. Extensive experiments conducted on four benchmark datasets demonstrate that CFADNet consistently outperforms existing state-of-the-art methods， achieving superior performance across key evaluation metric， including MAE， F-measure， and structural similarity index. These findings highlight the robustness and strong generalization capability of CFADNet in diverse and challenging environments. By combining attention-based feature fusion with effective edge refinement， CFADNet emerges as a powerful and reliable solution for RGB-D salient object detection into complex scenarios. Future research could explore extending this approach to other multi-modal tasks， such as RGB-Thermal or multi-spectral image processing， where challenges related to multi-modal fusion and boundary detection are also prevalent. Additionally， optimizing the computational efficiency of CFADNet for real-time deployment represents a potential research direction， enabling its application in time-sensitive applications such as autonomous driving and robotics.