Surface Defect Detection of Electric Power Equipment in Substation Based on Improved YOLOV4 Algorithm

Author:

Chen Xiaojiao1,An Zhenyu2,Huang Liansheng3,He Shiying1,Zhang Xiuqing1,Lin Shizhong4

Affiliation:

1. Chinese Academy of Sciences,Institute of Plasma Physics,Hefei,China

2. Purple Mountain Laboratories,Nanjing,China

3. University of Science and Technology of China,Institute of Plasma Physics,Chinese Academy of Sciences,Hefei,China

4. Anhui Transmission and Transformation Engineering Co., Ltd,Hefei,China

Publisher

IEEE

Cited by 22 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Typical Defect Detection in Substation Based on Improved DiffusionDet;2024 9th Asia Conference on Power and Electrical Engineering (ACPEE);2024-04-11

2. Substation instrument detection with improved YOLOv5 algorithm based on AI front-end;2024 3rd International Conference on Energy and Power Engineering, Control Engineering (EPECE);2024-02-23

3. Research on power equipment troubleshooting based on improved AlexNet neural network;Journal of Measurements in Engineering;2024-01-21

4. Dual Contrastive Training and Transferability-Aware Adaptation for Multisource Privacy-Preserving Motor Imagery Classification;IEEE Transactions on Instrumentation and Measurement;2024

5. Dual Graph Reasoning Network for Oil Leakage Segmentation in Substation Equipment;IEEE Transactions on Instrumentation and Measurement;2024

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