High temperature defect detection method for electrical equipment based on improved YOLOv5

Xu Guo; Yiming Zhang; Sen Zhou

doi:10.1117/12.3029067

22 May 2024 High temperature defect detection method for electrical equipment based on improved YOLOv5

Xu Guo, Yiming Zhang, Sen Zhou

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131762X (2024) https://doi.org/10.1117/12.3029067
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Faced with the large number of images of electrical equipment in electrical drone inspections, the efficiency of human visual recognition is low, and it is difficult to locate high-temperature defects in electrical equipment quickly. Therefore, this paper proposes a method for screening images of high-temperature defects in electrical equipment. Firstly, a target dataset is constructed, and data augmentation is applied to address the issue of imbalanced target samples. Then, improvements are made to the YOLOv5 network by adding a object detection layer to obtain larger feature maps. Finally, the modified YOLOv5 network is trained and compared with methods such as YOLOv5 and YOLOv4 through experimental studies. The experimental results demonstrate that the proposed method can effectively enhance the network's detection capabilities for targets and achieve accurate identification of electrical high-temperature defects, even under conditions with significant infrared interference.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xu Guo, Yiming Zhang, and Sen Zhou "High temperature defect detection method for electrical equipment based on improved YOLOv5", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131762X (22 May 2024); https://doi.org/10.1117/12.3029067

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