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Published in: Automatic Control and Computer Sciences 4/2023

01-08-2023

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

Authors: Dong Weiguang, Li Shengchang, Lu Haobo

Published in: Automatic Control and Computer Sciences | Issue 4/2023

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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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Metadata
Title
Multitarget Detection Algorithm of UHV Line Fitting Infrared Image Based on YOLOv5
Authors
Dong Weiguang
Li Shengchang
Lu Haobo
Publication date
01-08-2023
Publisher
Pleiades Publishing
Published in
Automatic Control and Computer Sciences / Issue 4/2023
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI
https://doi.org/10.3103/S0146411623040089

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