CenterNet-Elite: A Small Object Detection Model for Driving Scenario by Lingling Wang, Xiang Li, Xiaoyan Chen, Bin Zhou

“…To address the issue of feature information loss resulting from multiple downsamplings during feature extraction for small objects, we introduce the spatial and channel reconstruction convolution (SCConv) into the bottleneck to reduce spatial and channel redundancy and enhance feature representation. …”
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Boundary aware microscopic hyperspectral pathology image segmentation network guided by information entropy weight by Xueying Cao, Hongmin Gao, Ting Qin, Min Zhu, Ping Zhang, Ping Zhang, Peipei Xu, Peipei Xu

“…Specifically, we first propose a Laplacian of Gaussian operator convolution boundary feature extraction block, which encodes feature gradient information through the improved edge detection operators and emphasizes relevant boundary channel weights based on channel information entropy weighting. …”
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Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation by Xu Zhang, Gaoquan Gu

“…The approach first constructs a multi-scale self-calibrating convolutional neural network to aggregate input signals across different scales, adaptively establishing long-range spatial and inter-channel dependencies at each spatial location, thereby enhancing feature modeling under noisy conditions. …”
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Pavement pothole detection system based on deep learning and binocular vision by Tian Guan, Jianyuan Cai, Yu Wang, Wei Yang, Xiaobo Chang, Yi Han

“…The group attention shuffle block (GASB) is designed to enhance the expression of channel and spatial feature information in road images, while improving the existing shuffling network (ShuffleNetv2). …”
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Fast real-time detection and counting of thrips in greenhouses with multi-level feature attention and fusion by Zhangzhang He, Xinyue Chen, Ying Gao, Yu Zhang, Yuheng Guo, Tong Zhai, Xiaochen Wei, Huan Li, Haipeng Zhu, Yongkun Fu, Zhiliang Zhang, Zhiliang Zhang

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YOLO-WAS: A Lightweight Apple Target Detection Method Based on Improved YOLO11 by Xinwu Du, Xiaoxuan Zhang, Tingting Li, Xiangyu Chen, Xiufang Yu, Heng Wang

“…The YOLO-WAS model replaced the ordinary convolution module of YOLO11 with the Adown module proposed in YOLOv9, the backbone C3K2 module combined with Wavelet Transform Convolution (WTConv), and the spatial and channel synergistic attention module Self-Calibrated Spatial Attention (SCSA) combined with the C2PSA attention mechanism to form the C2PSA_SCSA module was also introduced. …”
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PDSDC: Progressive Spatiotemporal Difference Capture Network for Remote Sensing Change Detection by YeKai Cui, Peng Duan, Jinjiang Li

“…For high-level semantic features, a dynamic graph convolutional attention network is constructed, which dynamically establishes topological associations between features through a learnable adjacency matrix, optimizing global semantic consistency through a channel recalibration mechanism. …”
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A Ship’s Maritime Critical Target Identification Method Based on Lightweight and Triple Attention Mechanisms by Pu Wang, Shenhua Yang, Guoquan Chen, Weijun Wang, Zeyang Huang, Yuanliang Jiang

“…This structure forms a triple attention mechanism that accounts for the mutual dependencies between input channels and spatial positions, allowing for the calculation of attention weights for targets such as bridges, buoys, and other ships. …”
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Coalmine image super-resolution reconstruction via fusing multi-dimensional feature and residual attention network by Jian CHENG, Lifei MI, Hao LI, Heping LI, Guangfu WANG, Yongzhuang MA

“…First, a multi-branch network is employed to parallelly integrate dynamic convolution and channel attention mechanisms, capturing different spatial statistical characteristics through “horizontal-channel” and “vertical-channel” interactions. …”
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Tomato ripeness detection and fruit segmentation based on instance segmentation by Jinfan Wei, Yu Sun, Yu Sun, Lan Luo, Lingyun Ni, Mengchao Chen, Minghui You, Minghui You, Ye Mu, Ye Mu, He Gong, He Gong

“…In addition, this paper also introduces a partial self-attention module (PSA), which combines channel attention and spatial attention mechanism to capture global context information, improve the model’s ability to focus on the target region and extract details. …”
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GESC-YOLO: Improved Lightweight Printed Circuit Board Defect Detection Based Algorithm by Xiangqiang Kong, Guangmin Liu, Yanchen Gao

“…Second, the neck network employs the lightweight hybrid convolution GSConv. By integrating it with the VoV-GSCSP module, the Slim-neck structure is constructed. …”
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Dynamic Bidirectional Feature Enhancement Network for Thin Cloud Removal in Remote Sensing Images by Yu Wang, Hao Chen, Ye Zhang, Guozheng Li

“…First, we design a multidimensional attention module comprising skip-recursive dilated convolution-based spatial attention module and frequency-domain-driven channel attention module. …”
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TPDTNet: Two-Phase Distillation Training for Visible-to-Infrared Unsupervised Domain Adaptive Object Detection by Siyu Wang, Xiaogang Yang, Ruitao Lu, Shuang Su, Bin Tang, Tao Zhang, Zhengjie Zhu

“…Subsequently, a spatial-dimension convolution is integrated into the backbone network. …”
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Lightweight Dual-Backbone Detection Transformer for Infrared Insulator Detection by Boyang Zhang, Yanpeng Zhang, Liming Sun

“…Furthermore, the lightweight context gate block (CGBlock) is introduced into the model, improving the model’s spatial awareness and detection accuracy by integrating gated convolution mechanisms and adopting adaptive feature channel weighting strategies. …”
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Depth-Enhanced Tumor Detection Framework for Breast Histopathology Images by Integrating Adaptive Multi-Scale Fusion, Semantic Depth Calibration, and Boundary-Guided Detection by A. Robert Singh, Suganya Athisayamani, Hariharasitaraman S, Faten Khalid Karim, Jose Varela-Aldas, Samih M. Mostafa

“…SDICM constructs dense depth maps by fusing RGB, semantic, and sparse depth data through bidirectional feature aggregation, enhancing spatial continuity and edge clarity. …”
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Efficient Identification and Classification of Pear Varieties Based on Leaf Appearance with YOLOv10 Model by Niman Li, Yongqing Wu, Zhengyu Jiang, Yulu Mou, Xiaohao Ji, Hongliang Huo, Xingguang Dong

“…To address the issue of low recognition precision in Yuluxiang, the Spatial and Channel reconstruction Convolution (SCConv) module is introduced on the basis of YOLOv10 to improve the model. …”
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Tunnel Lining Recognition and Thickness Estimation via Optical Image to Radar Image Transfer Learning by Chuan Li, Tong Pu, Nianbiao Cai, Xi Yang, Hao Liu, Lulu Wang

“…The model integrates a convolutional block attention module (CBAM) to refine feature extraction by emphasizing critical characteristics of the two interface layers through channel-wise and spatial attention mechanisms. …”
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Dual Attention-Based Global-Local Feature Extraction Network for Unsupervised Change Detection in PolSAR Images by Dazhi Xu, Ming Li, Yan Wu, Peng Zhang, Xinyue Xin

“…However, convolution kernels with limited receptive fields have difficulty in exploring global information. …”
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Lightweight detection algorithms for small targets on unmanned mining trucks by Shuoqi CHENG, Yilihamu·YAERMAIMAITI, Lirong XIE, Xiyu LI, Ying MA

“…Additionally, it designs a detection decoupling head with a multi-head attention mechanism to improve the issue of network complexity caused by convolutional layer redundancy, processes spatial dimensions to focus on capturing target features, reduces interference from irrelevant backgrounds, and enhances the accuracy of occluded target recognition.urthermore, it constructs a lightweight neural network with dual convolution (CDC), enhancing inter-channel information flow, improving model feature expression capability, and reducing model complexity. …”
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MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection by Jianhua Ye, Pan Li, Yunda Zhang, Ze Guo, Shoujin Zeng, Youji Zhan

“…The removal of spatial and channel redundancy, in conjunction with interactive reconstruction, serves to simulate attention and enhance the learning ability of waterscape. …”
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