Infrared Image Detection of Electrical Equipment Based on Data Enhancement and Improved YOLOv5

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Infrared Image Detection of Electrical Equipment Based on Data Enhancement and Improved YOLOv5

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[1]LIAO Xiaohui,XIONG Zongyi,KONG Bin,et al.Infrared Image Detection of Electrical Equipment Based on Data Enhancement and Improved YOLOv5[J].Journal of Zhengzhou University (Engineering Science),2026,47(XX):1-8.[doi:10.13705/j.issn.1671-6833.2025.05.014]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 XX Page number: 1-8 Column: Public date: 2026-09-10

Title:: Infrared Image Detection of Electrical Equipment Based on Data Enhancement and Improved YOLOv5

Author(s):: LIAO Xiaohui; XIONG Zongyi; KONG Bin; XIE Zichen; LIU Xiangyang; GAO Ziyang; School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China

Keywords:: electrical equipment; data augmentation; GD mechanism; Focal-CIoU loss function ; infrared image recognition

CLC:: TP391.4TM63

DOI:: 10.13705/j.issn.1671-6833.2025.05.014

Abstract:: In order to make the detection method of heating defects of electrical equipment more perfect and improve the recognition accuracy of the algorithm for infrared images of electrical equipment, a method of infrared image detection of electrical equipment based on data enhancement and improved YOLOv5 was proposed. Firstly, for the problem of low signal-to-noise ratio and low contrast of infrared images of electrical equipment, a fast guided filtering algorithm was used to denoise the infrared images in the data set. The CLAHE algorithm was improved by introducing Gamma correction, and then the contrast of infrared images was enhanced. The premise of detecting the heating defects of electrical equipment was to accurately identify and classify the equipment. Then, in order to improve the accuracy of the detection algorithm, the information aggregation and distribution mechanism was introduced to improve the feature fusion module based on the original YOLOv5 algorithm, which enhanced the multi-scale feature fusion ability. Meanwhile, the Focal-CIoU loss function was also introduced to make the algorithm pay more attention to high-quality samples and suppress low-quality samples, which enhanced the rate of convergence for the model. It is verified that the mAP value of the improved algorithm on the self-built data set was 93.6%, which was 4.0 percentage point higher than that before the improvement. Compared with Faster R-CNN, SSD, YOLOv3 and YOLOv7, the mAP value of the proposed algorithm was increased by 4.5 percentage point, 6.1 percentage point, 4.7 percentage point and 3.5 percentage point respectively, and the frame rate reached 32 frames per second, which could meet the real-time recognition requirements of electrical equipment.

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Last Update: 2026-01-15