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Multitarget Detection Algorithm of UHV Line Fitting Infrared Image Based on YOLOv5

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Abstract

Gansu section of Qishao UHV DC line accounts for 52.4% of the total line length, and manual inspection is time consuming and laborious. This paper proposes an infrared image recognition method of hardware fittings based on YOLOv5 target detection. The image data set is constructed by using the infrared image of Qishao strain clamp of transmission operation inspection center, and the image annotation and data expansion are carried out. The YOLOv5 detection model was established, and trained by multistage transfer learning. Three methods were introduced to improve the training result, including Mosaic data enhancement, learning rate attenuation and label smoothing. The influences of different factors like anchor number, training method, and sample size, etc. on test results were analyzed, thus to obtain the optimal detection model. The image test set including insulator, equalizing ring and interval rods was detected, and the mean average precision (mAP) reaches 96.53%. Comparing the detection results of YOLOv5 with those of other commonly used models, it is found that the model in this paper has high accuracy and fast recognition speed, which provides a reference for fault inspection of Qishao UHV line.

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Authors and Affiliations

  1. School of Automation and Electrical Engineering of Lanzhou Jiaotong University, 730070, Lanzhou, China

    Dong Weiguang, Li Shengchang & Lu Haobo

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  1. Dong Weiguang
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  2. Li Shengchang
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  3. Lu Haobo
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Correspondence to Dong Weiguang, Li Shengchang or Lu Haobo.

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Dong Weiguang, Shengchang, L. & Haobo, L. Multitarget Detection Algorithm of UHV Line Fitting Infrared Image Based on YOLOv5. Aut. Control Comp. Sci. 57, 400–412 (2023). https://doi.org/10.3103/S0146411623040089

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