Estimating Canopy Chlorophyll Content of Potato Using Machine Learning and Remote Sensing by Xiaofei Yang, Hao Zhou, Qiao Li, Xueliang Fu, Honghui Li

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RST-YOLOv8: An Improved Chip Surface Defect Detection Model Based on YOLOv8 by Wenjie Tang, Yangjun Deng, Xu Luo

“…This integration effectively optimizes feature representation capabilities while significantly reducing the model’s parameter count. …”
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A Texture-Enhanced Deep Learning Network for Cloud Detection of GaoFen/WFV by Integrating an Object-Oriented Dynamic Threshold Labeling Method and Texture-Feature-Enhanced Attentio... by Bo Zhong, Xiao Tang, Xiaobo Luo, Shanlong Wu, Kai Ao

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WCANet: An Efficient and Lightweight Weight Coordinated Adaptive Detection Network for UAV Inspection of Transmission Line Accessories by Jiawei Chen, Pengfei Shi, Mengyao Xu, Yuanxue Xin, Xinnan Fan, Jinbo Zhang

“…The network is designed with a plug-and-play WCA module that can effectively identify dense small targets, retain information in each channel, and reduce computational overheads, while incorporating Sim-AFPN with a skip-connection structure into the network aggregate feature information layer by layer, enhancing the ability to capture key features, and achieving a lightweight network structure. …”
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HFEF²-YOLO: Hierarchical Dynamic Attention for High-Precision Multi-Scale Small Target Detection in Complex Remote Sensing by Yao Lu, Biyun Zhang, Chunmin Zhang, Yifan He, Yanqiang Wang

“…Existing methods often struggle to balance multi-scale feature enhancement and computational efficiency, particularly in scenarios with low target-to-background contrast. …”
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HGCS-Det: A Deep Learning-Based Solution for Localizing and Recognizing Household Garbage in Complex Scenarios by Houkui Zhou, Chang Chen, Zhongyi Xia, Qifeng Ding, Qinqin Liao, Qun Wang, Huimin Yu, Haoji Hu, Guangqun Zhang, Junguo Hu, Tao He

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MGL-YOLO: A Lightweight Barcode Target Detection Algorithm by Yuanhao Qu, Fengshou Zhang

“…Finally, a Lightweight Shared Multi-Scale Detection Head (LSMD) is proposed, which improves the model’s detection accuracy and adaptability while reducing the model’s parameter size and computational complexity. Experimental results show that the proposed algorithm increases MAP50 and MAP50.95 by 2.57% and 2.31%, respectively, compared to YOLOv8, while reducing parameter size and computational cost by 36.21% and 34.15%, respectively. …”
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LE-YOLO: A Lightweight and Enhanced Algorithm for Detecting Surface Defects on Particleboard by Chao He, Yongkang Kang, Anning Ding, Wei Jia, Huaqiong Duo

“…Current algorithms for surface defect detection in particleboard often encounter limitations such as high computational complexity and excessive parameter scale. …”
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Leveraging assistive technology for visually impaired people through optimal deep transfer learning based object detection model by Mahir Mohammed Sharif Adam, Nojood O. Aljehane, Mohammed Yahya Alzahrani, Samah Al Zanin

“…In recent times, deep learning (DL) techniques have become a powerful approach for extracting feature representations from data, leading to significant advancements in the field of object detection. …”
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YOLO-LSD: A Lightweight Object Detection Model for Small Targets at Long Distances to Secure Pedestrian Safety by Ming-An Chung, Sung-Yun Chai, Ming-Chun Hsieh, Chia-Wei Lin, Kai-Xiang Chen, Shang-Jui Huang, Jun-Hao Zhang

“…The proposed model integrates the C3C2 and the new Efficient Layer Aggregation Network - Convolutional Block Attention Module(ELAN-CBAM) modules to improve the efficiency of feature extraction while reducing computational overhead. …”
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AFN-Net: Adaptive Fusion Nucleus Segmentation Network Based on Multi-Level U-Net by Ming Zhao, Yimin Yang, Bingxue Zhou, Quan Wang, Fu Li

“…Specifically, our model achieved an IOU score of 0.8660 and a Dice score of 0.9216, with a model parameter size of only 7.81 M. These results illustrate that the method proposed in this paper not only excels in the segmentation of complex shapes and small targets but also significantly enhances overall performance at lower computational costs. …”
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Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

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Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues by Gianmarco Lazzini, Mario D’Acunto

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Evaluation of Mechanical Characteristics of Tungsten Inert Gas (TIG) Welded Butt Joint of Inconel 600 by Arash Moradi, Fatemeh Marashi Najafi, Yong Chen, Mahmoud Chizari

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DMCF-Net: Dilated Multiscale Context Fusion Network for SAR Flood Detection by Zhimin Wang, Lingli Zhao, Nan Jiang, Weidong Sun, Jie Yang, Lei Shi, Hongtao Shi, Pingxiang Li

“…Experimental results show that DMCF-Net outperforms other deep learning models, achieving an F1 score of 81.6% and an intersection over union of 68.9%, while also having lower computational cost (97.4G) and fewer parameters (16.4M).…”
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Accuracy–Efficiency Trade-Off: Optimizing YOLOv8 for Structural Crack Detection by Jiahui Zhang, Zoia Vladimirovna Beliaeva, Yue Huang

“…A lightweight C3Ghost module reduces parameter count and computation, while a bidirectional multi-scale feature fusion structure replaces the standard neck to enhance efficiency. …”
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BGLE-YOLO: A Lightweight Model for Underwater Bio-Detection by Hua Zhao, Chao Xu, Jiaxing Chen, Zhexian Zhang, Xiang Wang

“…The model has small parameters and low computational effort and is suitable for edge devices. …”
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An analytics-driven model for identifying autism spectrum disorder using eye tracking by Deblina Mazumder Setu

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An efficient supervised framework for music mood recognition using autoencoder‐based optimised support vector regression model by Gaurav Agarwal, Hari Om

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Filamentary Convolution for SLI: A Brain-Inspired Approach with High Efficiency by Boyuan Zhang, Xibang Yang, Tong Xie, Shuyuan Zhu, Bing Zeng

“…We propose filamentary convolution to replace rectangular kernels, reducing the parameters while preserving inter-frame features by focusing solely on frequency patterns. …”
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