FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. by Junjie Lu, Yuchen Zheng, Liwei Guan, Bing Lin, Wenzao Shi, Junyan Zhang, Yunping Wu

“…Firstly, the FasterNext module is proposed to reduce computational cost and enhance detection precision through lightweight design. …”
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A YOLOv8 algorithm for safety helmet wearing detection in complex environment by Chunning Song, Yinzhong Li

“…Second, a novel convolution module is proposed to help the network focus more on important feature information and improve the effectiveness of the model in feature extraction. …”
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A Dynamic Cascade Cross-Modal Coassisted Network for AAV Image Object Detection by Shu Tian, Li Wang, Lin Cao, Lihong Kang, Xian Sun, Jing Tian, Xiangwei Xing, Bo Shen, Chunzhuo Fan, Kangning Du, Chong Fu, Ye Zhang

“…To preserve multimodal fine-grained details, we devise a scale-adaptive dynamic feature prompt module, which dynamically motivates the backbone network to capture feature degradation clues. …”
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Wireless Authentication Method Based on Near-Field Feature Fusion Network by QIU Jiefan, ZHOU Kezhong, ZHU Dongfu, ZHANG Jinhong, CHI Kaikai

“…The WFFN consisted of three key modules: the dynamic feature extraction module, the LOS feature extraction module, and the feature fusion module. …”
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MASFF-Net: Multiazimuth Scattering Feature Fusion Network for SAR Target Recognition by Huiqiang Zhang, Wei Wang, Jie Deng, Yue Guo, Shengqi Liu, Jun Zhang

“…Subsequently, we propose a hierarchical multiscale feature extraction module to explore the global semantic and local detail features of the target. …”
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MVCL: Multiview Complementary Learning Network for Remote Sensing Image Change Detection by Andong Huang, Chuan Xu, Liye Mei, Zhiwei Ye, Wei Yang, Ying Wang, Xinghua Li

“…Furthermore, to mitigate the noise introduced during multiscale fusion in the nonchange (background) areas, we propose a prior-guided spatial background enhancement module, which consists of a prior semantic guidance strategy and a spatial background enhancement module. …”
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EPLC-Pose: A Lightweight Student Posture Recognition Network Under Panoramic Classroom by Yanhong Ji, Yibo Jin, Zuxuan Wang, Suyan Tan

“…The network’s backbone and neck components have been enhanced by replacing the traditional C2F module with the innovative C-UIB module. Due to the advanced design of the C-UIB module, the number of parameters has been significantly reduced. …”
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Rough-and-Refine Model for Scene Graph Generation by Li Junliang, Lv Shirong, Li Wei

“…The model represented in the first row of the table, which excludes all four modules, is equivalent to the Rough Part, showing an average decrease of 24.9%.ConclusionsTo address the issue of insufficient predicate representation, a scene graph generation method based on a Rough-and-Refine network is proposed. …”
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A Lightweight Model for Weed Detection Based on the Improved YOLOv8s Network in Maize Fields by Jinyong Huang, Xu Xia, Zhihua Diao, Xingyi Li, Suna Zhao, Jingcheng Zhang, Baohua Zhang, Guoqiang Li

“…Secondly, a four-stage inverted residual moving block (iRMB) was employed to construct a lightweight iDEMA module, which was used to replace the original C2f feature extraction module in the Neck to improve model performance and accuracy. …”
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A Spatial–Frequency Combined Transformer for Cloud Removal of Optical Remote Sensing Images by Fulian Zhao, Chenlong Ding, Xin Li, Runliang Xia, Caifeng Wu, Xin Lyu

“…The core of SFCRFormer is the spatial–frequency combined Transformer (SFCT) block, which implements cross-domain feature reinforcement through a dual-branch spatial attention (DBSA) module and frequency self-attention (FreSA) module to effectively capture global context information. …”
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A Reparameterization Feature Redundancy Extract Network for Unmanned Aerial Vehicles Detection by Shijie Zhang, Xu Yang, Chao Geng, Xinyang Li

“…We further enhance the adaptive nature of the Adown module by incorporating an adaptive spatial attention mechanism. …”
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SCite‐HRNet: A self‐calibrating efficient network for pose estimation by Nan Xiang, Xingdi Rao, Wenjing Yang, Jin Chen, Lifang Zhu

“…Ultimately, these two methodologies are incorporated into a multi‐scale information aggregation module and embed this module into the high‐resolution network. …”
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Dual-Branch Occlusion-Aware Semantic Part-Features Extraction Network for Occluded Person Re-Identification by Bo Sun, Yulong Zhang, Jianan Wang, Chunmao Jiang

“…Occlusion remains a major challenge in person re-identification, as it often leads to incomplete or misleading visual cues. To address this issue, we propose a dual-branch occlusion-aware network (DOAN), which explicitly and implicitly enhances the model’s capability to perceive and handle occlusions. …”
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Enhancing Point Cloud Classification and Segmentation With Attention-Enhanced SO-PointNet++ by Gang Cheng, Chengwei Gu

“…This module reduces the effect of the issue of gradient vanishing. …”
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Medical image segmentation model based on local enhancement driven global optimization by Lianghui Xu, Ayiguli Halike, Gan Sen, Mo Sha

“…Abstract In medical image segmentation, it is a challenging task to identify and locate the boundary of pathological tissue accurately. In response to this issue, this paper proposes a medical image segmentation model, named Local Enhancement Driven Global Optimization Network (LEGO-Net), and specially develops an Detail and Contour Recognition Module (DCRM) to accurately identify the boundaries of lesion tissue. …”
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Caricature-visual face recognition based on jigsaw solving and modal decoupling by Yajun Yao, Chongwen Wang

“…The CVF-JSM consists of two modules: feature extraction and decoupling. The feature extraction module incorporates a graph attention network at the intermediate stage of the backbone network, which constructs and solves jigsaw puzzles to enable the network to extract shape features. …”
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CSAN: A Channel–Spatial Attention-Based Network for Meteorological Satellite Image Super-Resolution by Weiliang Liang, Yuan Liu

“…The feature extraction module integrates channel and spatial attention into the residual network, enabling the extraction of informative spectral and spatial features from the fused inputs. …”
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Image denoising algorithm based on multi-channel GAN by Hongyan WANG, Xiao YANG, Yanchao JIANG, Zumin WANG

“…Aiming at the issue that the noise generated during image acquisition and transmission would degrade the ability of subsequent image processing, a generative adversarial network (GAN) based multi-channel image denoising algorithm was developed.The noisy color image could be separated into red-green-blue (RGB) three channels via the proposed approach, and then the denoising could be implemented in each channel on the basis of an end-to-end trainable GAN with the same architecture.The generator module of GAN was constructed based on the U-net derivative network and residual blocks such that the high-level feature information could be extracted effectively via referring to the low-level feature information to avoid the loss of the detail information.In the meantime, the discriminator module could be demonstrated on the basis of fully convolutional neural network such that the pixel-level classification could be achieved to improve the discrimination accuracy.Besides, in order to improve the denoising ability and retain the image detail as much as possible, the composite loss function could be depicted by the illustrated denoising network based on the following three loss measures, adversarial loss, visual perception loss, and mean square error (MSE).Finally, the resultant three-channel output information could be fused by exploiting the arithmetic mean method to obtain the final denoised image.Compared with the state-of-the-art algorithms, experimental results show that the proposed algorithm can remove the image noise effectively and restore the original image details considerably.…”
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YOLOv8-POS: a lightweight model for coal-rock image recognition by Yanqin Zhao, Wenyu Wang

“…The methodology introduces a C2f-PConv module, which ingeniously combines the strengths of C2f and partial convolution (PConv) to selectively process channels. …”
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TLINet: A defects detection method for insulators of overhead transmission lines using partially transformer block. by Xun Li, Yuzhen Zhao, Yang Zhao, Zhun Guo, Yongming Zhang, Xiangke Jiao, Baoxi Yuan

“…Next, a Dynamic Downsampling Module (DyDown) is introduced to mitigate the issue of small-scale defect information blurring. …”
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