Denoising and Feature Enhancement Network for Target Detection Based on SAR Images by Cheng Yang, Chengyu Li, Yongfeng Zhu

“…First, we design a background suppression and target enhancement module (BSTEM), which aims to suppress noise interference in complex backgrounds. …”
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HGNN-GAMS: Heterogeneous Graph Neural Networks for Graph Attribute Mining and Semantic Fusion by Yufei Zhao, Hua Liu, Hua Duan

“…The semantic aggregation module, leveraging an attention mechanism, integrates diverse semantic information within HGs. …”
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ETV-MVS: Robust Visibility-Aware Multi-View Stereo with Epipolar Line-Based Transformer by Shaoqian Wang, Xiaokun Ding, Yuxin Mao, Yuchao Dai

“…In this paper, we propose an innovative visibility-aware framework to address these issues. Central to our method is an Epipolar Line-based Transformer (ELT) module, which capitalizes on the epipolar line correspondence and candidate matching features between images to enhance the feature representation and correlation robustness. …”
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Lightweight network for insulator fault detection based on improved YOLOv5 by Dehua Weng, Zhiliang Zhu, Zhengbing Yan, Moran Wu, Ziang Jiang, Nan Ye

“…We designed a new module that optimises the computational complexity of networks and fused the module with the attention mechanism SimAM to solve the problem of low efficiency in detecting flashover faults. …”
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Underwater Image Enhancement Methods Using Biovision and Type-II Fuzzy Set by Yuliang Chi, Chao Zhang

“…In contrast, the method proposed in this paper applies a color correction module that takes into account the effects of biological vision in LAB color space, and an enhanced Type-II Fuzzy set visibility enhancement module. …”
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Detection method of potato leaf disease based on YOLOv5s by Jingtao Li, Hao Chen, Guisong Li, Yueqi Liu, Yanli Yang, Xia Liu, Chang Yi Wang

“… An improved leaf target detection method based on the YOLOv5s network is proposed to address the issues of low model detection accuracy and slow detection speed in potato leaf image target detection. …”
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Face Anti-Spoofing Based on Adaptive Channel Enhancement and Intra-Class Constraint by Ye Li, Wenzhe Sun, Zuhe Li, Xiang Guo

“…The ECA module extracts features through deep convolution, while the Bottleneck Reconstruction Module (BRM) employs a channel compression–expansion mechanism to refine spatial feature selection. …”
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STBNA-YOLOv5: An Improved YOLOv5 Network for Weed Detection in Rapeseed Field by Tao Tao, Xinhua Wei

“…The ideal method for applying herbicides would be selective variable spraying, but the primary challenge lies in automatically identifying weeds. To address the issues of dense weed identification, frequent occlusion, and varying weed sizes in rapeseed fields, this paper introduces a STBNA-YOLOv5 weed detection model and proposes three enhanced algorithms: incorporating a Swin Transformer encoder block to bolster feature extraction capabilities, utilizing a BiFPN structure coupled with a NAM attention mechanism module to efficiently harness feature information, and incorporating an adaptive spatial fusion module to enhance recognition sensitivity. …”
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Toward general object search in open reality by Gang Shen, Wenjun Ma, Guangyao Chen, Yonghong Tian

“…Moreover, an Open Score Fusion (OSF) module is integrated into SEA-Net during inference to yield a more robust matching score in open-world scenarios. …”
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Adaptive Window Multi-Feature Fusion Point Cloud Semantic Segmentation Network by Lie Zhu

“…The fixed window employs a local relative attention feature expansion module to extract fine-grained local features. Additionally, the method improves edge feature recognition during the upsampling stage through an inter-layer edge enhancement and suppression module. …”
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An Improved YOLOv7-Tiny-Based Algorithm for Wafer Surface Defect Detection by Mengyun Li, Xueying Wang, Hongtao Zhang, Xiaofeng Hu

“…First, a coordinate attention (CA) module is incorporated into the feature extraction network to enhance the network’s ability to learn features at defect locations. …”
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Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution by Shizhao Li, Zhichao Yan, Boxiang Ma, Shaoru Guo, Hongxia Song

“…However, existing semantic segmentation methods often suffer from issues such as low precision and slow inference speed. …”
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Visible-infrared person re-identification with region-based augmentation and cross modality attention by Yuwei Guo, Wenhao Zhang, Licheng Jiao, Shuang Wang, Shuo Wang, Fang Liu

“…Moreover, a lightweight hybrid compensation module, i.e., a Modality Feature Transfer (MFT) module, is proposed to integrate cross attention and convolution networks to avoid introducing interfering information while preserving minimal computational overhead. …”
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PIFRNet: Position Information Guided Feature Reconstruction Network for Salient Object Detection in Remote Sensing Images by Zhen Wang, Ruixiang Li, Xiaotian Wang, Nan Xu, Zhuhong You

“…However, salient object detection for remote sensing images (RSI-SOD) faces unique challenges, including high resolution, diverse object scales, and cluttered backgrounds, which limit the effectiveness of existing methods. To overcome these issues, we propose a Position Information Guided Feature Reconstruction Network, where each module is specifically designed to address a core RSI-SOD challenge. …”
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LocaLock: Enhancing Multi-Object Tracking in Satellite Videos via Local Feature Matching by Lingyu Kong, Zhiyuan Yan, Hanru Shi, Ting Zhang, Lei Wang

“…Additionally, the local computation within the LCV module ensures low computational complexity and memory usage. …”
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Deep-Learning-Based Automated Building Construction Progress Monitoring for Prefabricated Prefinished Volumetric Construction by Wei Png Chua, Chien Chern Cheah

“…WAVBCPM is segregated into three modules. A detection module first conducts detection of windows on the target building. …”
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Multi-Strategy Enhancement of YOLOv8n Monitoring Method for Personnel and Vehicles in Mine Air Door Scenarios by Lei Zhang, Hongjing Tao, Zhipeng Sun, Weixun Yi

“…Firstly, the Faster Block module, which incorporates partial convolution (PConv), is integrated with the C2f module of the backbone network. …”
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SR-YOLO: Spatial-to-Depth Enhanced Multi-Scale Attention Network for Small Target Detection in UAV Aerial Imagery by Shasha Zhao, He Chen, Di Zhang, Yiyao Tao, Xiangnan Feng, Dengyin Zhang

“…First, the Space-to-Depth layer and Receptive Field Attention Convolution are combined, and the SR-Conv module is designed to replace the Conv module within the original backbone network. …”
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EHAFF-NET: Enhanced Hybrid Attention and Feature Fusion for Pedestrian ReID by Jun Yang, Yan Wang, Haizhen Xie, Jiayue Chen, Shulong Sun, Xiaolan Zhang

“…The spatial attention mechanisms aggregate features using global average and max pooling to enhance spatial representation. To tackle issues like perspective differences, lighting changes, and occlusions, we incorporate the Multi-Branch Feature Integration module. …”
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CCMCS-Net: Integrating Color Correction and Multicolor-Space Stretching for Improving Underwater Image Quality by Jianjun Chen, Yujie Yang, Jingjing Song, Xibei Yang, Jinlong Shi

“…However, underwater images frequently face issues, such as color loss and diminished contrast. …”
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