YOLO-SRW: An Enhanced YOLO Algorithm for Detecting Prohibited Items in X-Ray Security Images by Minwei Chen, Zhixian Zhang, Nian Jiang, Xingxing Li, Xin Zhang

“…To address the challenges of false positives and false negatives in prohibited item detection within X-ray security images, caused by complex backgrounds, poor image quality, and varying scales, this paper proposes an improved algorithm based on YOLOv8, named YOLO-SRW, to improve the accuracy of detecting prohibited items. …”
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Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms by Ye Liu, Shixin Li, Liming Zhou, Haichen Liu, Zhiyu Li

“…To address these challenges, this paper proposes a low-light object detection algorithm named Dark-YOLO, which dynamically extracts features. …”
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CSCP-YOLO: A Lightweight and Efficient Algorithm for Real-Time Steel Surface Defect Detection by Chenglong Wang, Heng Wang, Yimin Jiang, Lei Yu, Xueting Wang

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GU-Net3+: A Global-Local Feature Fusion Algorithm for Building Extraction in Remote Sensing Images by Yali Liu, Cui Ni, Peng Wang, Dongqing Yang, Hexin Yuan, Chao Ma

“…In this study, we propose a building detection method that integrates global and local features. …”
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Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior by Huahua CHEN, Zhe CHEN

“…Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.…”
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Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior by Huahua CHEN, Zhe CHEN

“…Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks, an anomaly detection model based on sparse variational autoencoder was proposed.Firstly, the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder, simulated the sparsity of the space where the hidden variables were located, and obtained the sparse representation of data characteristics.Secondly, combined with the deep support vector network, the feature space was compressed, and the optimal hypersphere was found to discriminate normal data and abnormal data.And then, the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere, and then the abnormal detection was carried out.Finally, the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.…”
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Adaptive convolutional neural network-based principal component analysis algorithm for the detection of manufacturing data by Tsun-Kuo Lin

“…This adaptive algorithm is capable of learning from limited image signals or features, enhancing data interpretability and increasing the amount of feature information for detecting manufacturing data.…”
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An Improved Lithium-Ion Battery Fire and Smoke Detection Method Based on the YOLOv8 Algorithm by Li Deng, Di Kang, Quanyi Liu

“…This paper introduces a novel algorithm—YOLOv8 (You Only Look Once version 8) + FRMHead (a multi-branch feature refinement head) + Slimneck (a lightweight bottleneck module), abbreviated as YFSNet—for lithium-ion battery fire and smoke detection in complex backgrounds. …”
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DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETR by Xiaolong Wei, Ling Yin, Liangliang Zhang, Fei Wu

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YOLOv8s-SNC: An Improved Safety-Helmet-Wearing Detection Algorithm Based on YOLOv8 by Daguang Han, Chunli Ying, Zhenhai Tian, Yanjie Dong, Liyuan Chen, Xuguang Wu, Zhiwen Jiang

“…To address this challenge, we propose YOLOv8s-SNC, an improved YOLOv8 algorithm for robust helmet detection in industrial scenarios. …”
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Research on surface loss detection algorithm of wind turbine blade based on FRE-DETR network by Jingwei Yang, Xiaocong Chen, Shengxian Cao, Bo Zhao, Zhenhao Tang, Gong Wang, Xingyu Li, Han Gao

“…Wind turbine generators operate in harsh areas for a long time, resulting in frequent problems such as blade breakage, and traditional blade defect detection methods have low detection accuracy. In this paper, an end-toend target detection algorithm FRE-DETR based on wind turbine blade defects is designed, and the detection speed and detection accuracy of the end-to-end detection model are further improved by redesigning the feature extraction location in the backbone network and proposing a feature selection and fusion module. …”
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Small Target Detection Algorithm for UAV Aerial Images Based on Improved YOLOv7-tiny by ZHANG Guanghua, LI Congfa, LI Gangying, LU Weidang

“…Comparative visual detection results with the baseline YOLOv7-tiny show that the proposed algorithm significantly improves the identification of multi-scale small targets and reduces both missed and false detections.…”
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Unsupervised Anomaly Detection on Metal Surfaces Based on Frequency Domain Information Fusion by Wenfei Wu, Tao Tao, Jinsheng Xiao, Yichu Yao, Jianfeng Yang

“…The limited number of defect samples, unpredictable defect characteristics, and the interference of metal grain bring great challenges to metal surface defect detection. For this reason, this paper proposes an unsupervised algorithm, FFnet, based on the fusion of frequency domain information, which introduces the frequency domain features into the unsupervised detection. …”
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A Lightweight Algorithm for Detection and Grading of Olive Ripeness Based on Improved YOLOv11n by Fengwu Zhu, Suyu Wang, Min Liu, Weijie Wang, Weizhi Feng

“…To address these limitations, this study proposes a lightweight algorithm for detection and grading of olive ripeness based on an Improved YOLOv11n framework. …”
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YOLOv10-LGDA: An Improved Algorithm for Defect Detection in Citrus Fruits Across Diverse Backgrounds by Lun Wang, Rong Ye, Youqing Chen, Tong Li

“…We propose an improved YOLOv10-based disease detection method that replaces the traditional convolutional layers in the Backbone network with LDConv to enhance feature extraction capabilities. …”
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MSFA-YOLO: A Multi-Scale SAR Ship Detection Algorithm Based on Fused Attention by Zhao Liangjun, Ning Feng, Xi Yubin, Liang Gang, He Zhongliang, Zhang Yuanyang

“…Leveraging the excellent feature representation capabilities of neural networks, deep learning methods have been widely adopted for object detection in synthetic aperture radar (SAR) images. …”
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Research and Application of a Multitarget Detection Algorithm Based on Improved YOLOv8 for Indoor Objects by Yanzhen Wang, Wei Wang, Xiaolong Zhou, Xubin Dong, Jianyong Li, Qi Zhao, Xinyu Yang, Yao Wang

“…The goal is to achieve efficient multitarget detection of everyday objects and to improve the accuracy and recall of the detection algorithm. …”
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Feature Selection based on Genetic Algorithm for Classification of Mammogram Using K-means, k-NN and Euclidean Distance by Kameran Adil Ibrahim

“…., the classifications was done on the bases of the features selected using genetic algorithm. Attempts have also been made to study the performance of each feature selected by Genetic Algorithm (GA) in classification. …”
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A non-intrusive load monitoring algorithm based on real-time feature extraction and deep learning model by Behrooz Taheri, Mostafa Sedighizadeh, Mohammad Reza Nasiri, Alireza Sheikhi Fini

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Rice-SVBDete: a detection algorithm for small vascular bundles in rice stem’s cross-sections by Xiaoying Zhu, Weiyu Zhou, Jianguo Li, Jianguo Li, Mingchong Yang, Mingchong Yang, Haiyu Zhou, Haiyu Zhou, Jiada Huang, Jiada Huang, Jiahua Shi, Jun Shen, Guangyao Pang, Lingqiang Wang, Lingqiang Wang, Lingqiang Wang

“…Accurate measurement of their structure and distribution is essential for improving rice breeding and cultivation strategies. However, the detection of small vascular bundles from cross-sectional images is challenging due to their tiny size and the noisy background typically present in microscopy images.MethodsTo address these challenges, we propose Rice-SVBDete, a specialized deep learning-based detection algorithm for small vascular bundles in rice stem cross-sections. …”
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