An infrared image detection method of abnormal hot spot of distribution equipment based on YOLOv4 algorithm

Yi Ding; Fei Fei Teng; Chao Pang; Pan Zhang; Pei Chen; Xianxu Huo

doi:10.1117/12.3004446

25 September 2023 An infrared image detection method of abnormal hot spot of distribution equipment based on YOLOv4 algorithm

Yi Ding, Fei Fei Teng, Chao Pang, Pan Zhang, Pei Chen, Xianxu Huo

Author Affiliations +

Proceedings Volume 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023); 127883Y (2023) https://doi.org/10.1117/12.3004446
Event: Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Power equipment is the cornerstone of power system. Due to the advantages of infrared detection technology such as non-contact detection, power grid companies often use infrared detection technology to conduct regular inspection of power equipment. However, at present, the efficiency of manual inspection is low, and it is easy to be affected by the subjective experience and working time of inspection personnel, resulting in the misjudgment of the status of power equipment, which will be difficult to meet the requirements of real-time processing of infrared data of a large number of power equipment in the future. In addition, there are still many problems in the infrared image detection and recognition process of power equipment, such as low contrast of infrared image, mutual occlusion of power equipment and imbalance of positive and negative samples in the image. Based on the above difficulties, this paper develops the research of infrared image detection of power equipment based on deep convolutional neural network. This paper builds a power equipment infrared image detection network based on YOLOv4 algorithm in the Pytorch deep learning framework. Through the verification and effect testing of the proposed algorithm model, the results show that the proposed detection algorithm has realized real-time detection and achieved high detection accuracy. This lays a foundation for the realization of automatic and intelligent infrared image detection and recognition of power equipment, and lays a foundation for the subsequent state diagnosis of power equipment.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yi Ding, Fei Fei Teng, Chao Pang, Pan Zhang, Pei Chen, and Xianxu Huo "An infrared image detection method of abnormal hot spot of distribution equipment based on YOLOv4 algorithm", Proc. SPIE 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 127883Y (25 September 2023); https://doi.org/10.1117/12.3004446

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KEYWORDS

Infrared imaging

Infrared radiation

Infrared detectors

Thermography

Detection and tracking algorithms

Education and training

Image processing

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