AC-YOLO: citrus detection in the natural environment of orchards by Xu Xiao, Yaonan Wang, Yiming Jiang, Haotian Wu, Zhe Zhang, Rujing Wang

“…Firstly, in the Resblock module of the YOLOv4 backbone feature extraction network, the AC network structure is integrated with different levels of feature mapping to fuse context information as small targets. …”
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Lightweight highland barley detection based on improved YOLOv5 by Minghui Cai, Hui Deng, Jianwei Cai, Weipeng Guo, Zhipeng Hu, Dongzheng Yu, Houxi Zhang

“…In addition, the integration of convolutional block attention module (CBAM) enhances the model’s ability to focus on target object in complex backgrounds. …”
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ASCDet: cross-space UAV object detection method guided by adaptive sparse convolution by Gui Cheng, Xubin Feng, Yan Tian, Meilin Xie, Chaoya Dang, Qing Ding, Zhenfeng Shao

“…These masks guide cross-space object detection through sparse convolutions, while a global context enhancement strategy within the sparse convolution module enriches the contextual information, maintaining detection accuracy. …”
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SPL-PlaneTR: Lightweight and Generalizable Indoor Plane Segmentation Based on Prompt Learning by Zhongchen Deng, Yuanlong Ge, Xiatian Qi, Kai Sun, Ruixi Wan, Bingxu Zhang, Shenman Zhang, Xun Zhang, Yan Meng

“…In this study, we propose an improved version of PlaneTR, named Spatial Prompt Learning PlaneTR (SPL-PlaneTR), to address these issues. Our approach effectively balances model complexity and performance. …”
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Parallax-Tolerant Weakly-Supervised Pixel-Wise Deep Color Correction for Image Stitching of Pinhole Camera Arrays by Yanzheng Zhang, Kun Gao, Zhijia Yang, Chenrui Li, Mingfeng Cai, Yuexin Tian, Haobo Cheng, Zhenyu Zhu

“…However, existing solutions can only address specific color difference issues and are ineffective for pinhole images with parallax. …”
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YOLOLS: A Lightweight and High-Precision Power Insulator Defect Detection Network for Real-Time Edge Deployment by Qinglong Wang, Zhengyu Hu, Entuo Li, Guyu Wu, Wengang Yang, Yunjian Hu, Wen Peng, Jie Sun

“…However, deploying deep learning models on edge devices presents significant challenges due to limited computational resources and strict latency constraints. To address these issues, we propose YOLOLS, a lightweight and efficient detection model derived from YOLOv8n and optimized for real-time edge deployment. …”
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Infrared Small Target Detection Based on Weak Feature Enhancement and Target Adaptive Proliferation by Xiaoyu Xu, Weida Zhan, Yichun Jiang, Depeng Zhu, Yu Chen, Jinxin Guo, Ziqiang Hao, Deng Han

“…The imbalance between positive and negative samples and the loss of small targets in complex backgrounds are catastrophic for infrared small target detection. To address these issues, we proposed an infrared small target detection method based on weak feature enhancement and target adaptive proliferation (IRSTD-WFETAP). …”
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Implementation of a Digital Model of Thermal Characteristics Based on the Temperature Field by V. V. Pozevalkin, A. N. Polyakov

“…However, the scientific literature does not widely present the results of research aimed at implementing digital twin technology in the design process. The general issues related to the use of digital twins in various industries are mainly considered. …”
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SRW-YOLO: A Detection Model for Environmental Risk Factors During the Grid Construction Phase by Yu Zhao, Fei Liu, Qiang He, Fang Liu, Xiaohu Sun, Jiyong Zhang

“…Second, we integrate a reparameterized convolution based on channel shuffle (RCS) of a one-shot aggregation (RCS-OSA) module into the backbone and neck’s shallow layers, enhancing feature extraction while significantly reducing inference latency. …”
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Joint Classification of Hyperspectral and LiDAR Data via Multiprobability Decision Fusion Method by Tao Chen, Sizuo Chen, Luying Chen, Huayue Chen, Bochuan Zheng, Wu Deng

“…In the multifeature extraction module, the local texture features and spatial features of the image are extracted to consider the local texture and spatial structure of the image data. …”
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Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge by Sanggeon Yun, Ryozo Masukawa, Hanning Chen, Sungheon Jeong, Wenjun Huang, Arghavan Rezvani, Minhyoung Na, Yoshiki Yamaguchi, Mohsen Imani

“…Utilizing a Fast Fourier Transform (FFT) module, convolutional neural network (CNN) layers, and HyperDimensional Computing (HDC), our model excels in low-energy, rapid inference, and online learning. …”
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Multilevel Feature Cross-Fusion-Based High-Resolution Remote Sensing Wetland Landscape Classification and Landscape Pattern Evolution Analysis by Sijia Sun, Biao Wang, Zhenghao Jiang, Ziyan Li, Sheng Xu, Chengrong Pan, Jun Qin, Yanlan Wu, Peng Zhang

“…To alleviate the semantic confusion caused by different-level features with semantic gaps during fusion, we introduce a deep–shallow feature cross-fusion (DSFCF) module between the encoder and the decoder. We incorporate global–local attention block (GLAB) to aggregate global contextual information and local detail. …”
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Low-light image enhancement method for underground mines based on an improved Zero-DCE model by WANG Yiwei, LI Xiaoyu, WENG Zhi, BAI Fengshan

“…A Convolutional Block Attention Module (CBAM) was introduced at the skip connections between the shallow and deep networks of the Zero-DCE model to enhance the model's ability to capture key image features. …”
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ATT-CR: Adaptive Triangular Transformer for Cloud Removal by Yang Wu, Ye Deng, Pengna Li, Wenli Huang, Kangyi Wu, Xiaomeng Xin, Jinjun Wang

“…However, they suffer from the following issues: 1) the high computational complexity of self-attention limits scalability; 2) treating both cloudy and clean pixels as valid within the attention computation brings disturbances in subsequent layers, leading to suboptimal performance. …”
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PEYOLO a perception efficient network for multiscale surface defects detection by Xun Li, Yuzhen Zhao, Xiangke Jiao, Qingzhe Meng, Zhun Guo, Ruijuan Yao, Yaqiao Yang, Baoxi Yuan

“…These three modules can be seamlessly integrated into the YOLO framework. …”
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BinaryViT: Binary Vision Transformer for Hyperspectral Image Classification by Xiang Hu, Taolin Liu, Zhe Guo, Yuxiang Tang, Yuanxi Peng, Tong Zhou

“…Built upon traditional Transformers, the approach innovatively introduces a self-adaptive softmax binarization module, which dynamically adjusts the binarization threshold distribution to effectively mitigate discretization errors in gradient propagation during the binarization process. …”
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A Patch-Wise Mechanism for Enhancing Sparse Radar Echo Extrapolation in Precipitation Nowcasting by Yueting Wang, Hou Jiang, Tang Liu, Ling Yao, Chenghu Zhou

“…This limitation hinders accurate predictions of low-frequency heavy rainfall and localized precipitation events. To address these issues, a novel Patch-wise (PW) mechanism is proposed in this study. …”
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STDF: Joint Spatiotemporal Differences Based on xLSTM Dendritic Fusion Network for Remote Sensing Change Detection by Yu Zhou, Shengning Zhou, Dapeng Cheng, Jinjiang Li, Zhen Hua

“…In addition, we introduce a hierarchical dendritic coordination module based on the dendritic neuron model, which leverages a dynamic weighting mechanism to flexibly integrate multiscale feature maps. …”
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MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection by Hantao Ding, Shengfeng Lou, Hairong Ye, Yanbing Chen

“…Existing methods are often plagued by modality discrepancies and fragmented temporal reasoning. To address these issues, we introduce MT-CMVAD, a hierarchically structured Transformer architecture that makes two key technical contributions: (1) A Context-Aware Dynamic Fusion Module that leverages cross-modal attention with learnable gating coefficients to effectively bridge the gap between RGB and optical flow modalities through adaptive feature recalibration, significantly enhancing fusion performance; (2) A Multi-Scale Spatiotemporal Transformer that establishes global-temporal dependencies via dilated attention mechanisms while preserving local spatial semantics through pyramidal feature aggregation. …”
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Deep Learning Extraction of Tidal Creeks in the Yellow River Delta Using GF-2 Imagery by Bojie Chen, Qianran Zhang, Na Yang, Xiukun Wang, Xiaobo Zhang, Yilan Chen, Shengli Wang

“…Adding coordinate attention (CA) to the Atrous Spatial Pyramid Pooling (ASPP) module improves target classification and localization in remote sensing images. …”
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