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.

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.

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.

Facial age estimation from images constitutes a prominent area of research within the field of computer vision， offering extensive potential applications in fields such as biometrics， digital marketing， healthcare， and human-computer interaction. Despite substantial efforts by numerous researchers in this field， achieving accurate facial age estimation remains a formidable challenge， primarily due to the lack of high-quality， large-scale labeled datasets for facial age estimation. The manual annotation of facial datasets necessitates considerable time and financial costs. Semi-supervised learning has emerged as a promising strategy for solving this problem because it enables the simultaneous utilization of labeled and unlabeled data. However， achieving satisfactory results in the domain of facial age estimation using semi-supervised learning methods is difficult. This difficulty arises from the limited accuracy of the pseudo-labels produced by these methods， as well as their susceptibility to the influence of outlier data. These factors hinder the effective utilization of unlabeled data， consequently limiting overall performance. Aiming to address these challenges， optimizing the capability of the model to extract features is essential. Such improvements will facilitate the effective acquisition of valuable representations from unlabeled data， thereby yielding highly precise pseudo-labels. Additionally， establishing a semi-supervised learning framework that can adeptly manage the challenges associated with outlier data while optimizing the utilization of the unlabeled dataset is crucial. Consequently， this study presents an open-set semi-supervised multi-task approach for facial age estimation.

This research presents the SwinLEDF model to optimize the capability of the model to extract local and global features from facial images. This model is based on the Swin Transformer architecture and integrates local enhanced feedforward （LEFF） modules along with dynamic filter networks （DFNs）. The Swin Transformer demonstrates proficient capabilities in capturing long-range dependencies and global characteristics， particularly in the analysis of age-related trends and the overall morphology of facial structures. The LEFF module incorporates non-linear transformations at the feature level， facilitating the identification of local patterns within images or feature representations. This capability is essential for differentiating age-related attributes， including intricate details such as wrinkles and skin texture. The DFN module implements a dynamic filtering operation within the spatial dimension of the model’s output， thereby enhancing model flexibility and adaptability. Furthermore， this research presents an open-set semi-supervised multitask learning algorithm to optimize the use of labeled and unlabeled data. In this algorithm， the model assesses the probability of unlabeled data being classified as outliers by integrating the outcomes of a closed-set classifier and a multi-class binary classifier. Subsequently， the model generates pseudo-labels for non-outlier data that meet a specified confidence threshold. Additionally， the model simultaneously learns to estimate sex， race， and age using labeled and unlabeled data. Through this process， the model learns not only the unique characteristics associated with each specific task but also the interrelationships among gender， race， and age， thereby enhancing the capability of the model to process diverse data and increases its expressive power and robustness. Furthermore， the process enables the effective utilization of unlabeled datasets， addressing the challenge of limited labeled data in the field of age estimation. This study employs an adaptive threshold mechanism and a negative learning strategy to optimize the use of unlabeled data. The adaptive threshold mechanism dynamically adjusts the confidence threshold for pseudo-labels based on the model’s training performance across different categories， effectively addressing category imbalance and improving the precision of pseudo-label production. The negative learning strategy enhances the handling of unlabeled data by identifying categories to which the input data does not belong， thereby mitigating the adverse effects of false pseudo-labels on model performance.

This study assesses the proposed methodology using the MORPH and UTKface datasets. On the MORPH dataset， the model exhibits a mean absolute error （MAE） of 1.908 when trained solely on labeled data. This error is further reduced to 1.885 with the inclusion of labeled and unlabeled datasets. Similarly， for the UTKface dataset， the initial MAE is recorded at 4.343 using only labeled datasets， which subsequently reduces to 4.246 following the integration of labeled and unlabeled datasets. Compared to current facial age estimation methods， the proposed approach exhibits superior performance and further optimizes its accuracy by leveraging unlabeled facial datasets.

This study introduces an open-set semi-supervised multi-task learning method for facial age estimation. The proposed method effectively extracts gender， race， and age attributes from facial images while leveraging unlabeled data and appropriately handling potential outliers. This approach addresses the challenges associated with limited labeled data， thereby enhancing the accuracy of facial age estimation. Furthermore， the methodology presents innovative strategies for achieving precise results and holds strong potential for practical applications.

The objective of this research is to enhance the quality and accuracy of information extracted from coal mine images， which are often degraded by high dust concentrations and uneven lighting conditions. These challenging environmental conditions introduce noise， reduce local contrast， and lead to the loss of fine details and edge textures， ultimately compromising the visual quality and the reliability of information extraction. Aiming to address these challenges， this study proposes a self-supervised coal mine image denoising algorithm based on adaptive masking. Designed to handle a wide range of noise levels and types， this algorithm aims to restore the original integrity of the image while preserving critical visual features. The proposed algorithm is divided into three main components： adaptive masking， mask integration， and an adaptive integrated loss function. Each component plays a vital role in enhancing the denoising process， ensuring that the final output is accurate and visually appealing.

The adaptive masking component is the cornerstone of the proposed algorithm， enabling segmented processing of coal mine images. This segmentation not only reduces computational overhead but also allows for more targeted and effective denoising. By dividing each image into smaller blocks， the algorithm can analyze and process each section independently， thereby improving the overall efficiency of the denoising process. The module operates by sequentially applying a mask to the edge and corner pixels of each block， while deliberately excluding the central pixels. This method prevents the network from performing a trivial identity mapping that fails to enhance image quality. Instead， this approach introduces data variability that boosts the generalization capability and robustness of the neural network model， making it adaptable to previously unknown images. The adaptive nature of the mask ensures that the module responds dynamically to varying noise levels and image features. By analyzing local variance and texture complexity， the mask can adaptively determine the optimal masking strategy for each block. This tailored approach ensures that the denoising process is responsive to the specific characteristics of each image， substantially improving its effectiveness. Subsequently， once the masking process is complete， the mask integration module is employed. This module is responsible for fusing the neural network’s output with the masked areas to reconstruct a coherent and denoised image. The integration involves calculating the Hadamard product （element-wise multiplication） between the network’s output and the masked image. This strategic operation enhances the network’s capability to distinguish between actual image content and noise， especially around edges and texture boundaries. In this stage， considering local and global features of the coal mine images is crucial. Effective integration of these features allows the algorithm effectively interpret image context， leading in denoised outputs that are coherent and structurally complete. The mask integration module also ensures that denoised areas seamlessly blend into the rest of the image， preserving the overall visual flow and structural integrity. Furthermore， this module incorporates a quality evaluation mechanism to assess the effectiveness of the integration. The feedback from these evaluations is used to iteratively refine the integration process. The final component of the algorithm is an adaptive integrated loss function， which guides the model during training. This loss function is specifically designed to address the unique challenges of coal mine image denoising， including complex noise patterns and the need to preserve subtle image details. The adaptive integrated loss uses the integrated image as a training label， allowing the model to learn effectively from the differences between the noisy input images and the denoised outputs. Additionally， by incorporating the original noisy image， the loss function increases the model’s sensitivity to signal changes， enhancing its adaptability across various denoising scenarios and noise conditions.

The proposed algorithm was rigorously tested using an underground coal mine image dataset alongside four additional public datasets， including Kodak24 （Kodak lossless true color image suite）， BSD300 （Berkeley segmentation dataset 300）， and BSDS500 （Berkeley segmentation dataset 500）. The experiments were specifically designed to simulate real-world conditions， with a particular emphasis on dimly lit environments commonly encountered in coal mines. The results of these experiments demonstrated that the algorithm substantially outperformed other comparative denoising algorithms， in terms of subjective evaluations and objective metrics such as peak signal-to-noise ratio （PSNR） and structural similarity index （SSIM）. In tunnel scenes with a high level of Gaussian noise （level 50）， the algorithm achieved substantial improvements in PSNR/SSIM values compared to existing methods such as B2U and NBR2NBR， with increases of 4.2 dB/0.055 and 2.99 dB/0.077， respectively. Furthermore， when tested on images corrupted with Gaussian noise levels ranging from 5 to 50 on the public datasets， the algorithm consistently demonstrated substantial PSNR improvements over the second-best method， with increases of 1.09%， 0.72%， and 0.68% for Kodak24， BSD300， and BSDS500， respectively.

The proposed self-supervised denoising algorithm has demonstrated a strong capability to remove noise while preserving overall image information from single coal mine images， across various noise levels and types. This finding highlights the algorithm’s robustness and generalization capabilities， making it a promising tool for real-world applications in coal mine monitoring and safety systems. The effectiveness of the algorithm in enhancing image quality and improving the accuracy of information extraction， even under challenging conditions， underscores its potential to make a substantial contribution to the field of coal mine image processing and analysis.The code in this paper can be obtained by https://www.sciclb.cn/anonymous/skpswk56.

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.

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.