Detection method based on improved faster R-CNN for pin defect in transmission lines

Defects such as insulator, pins, and counterweight in highvoltage transmission lines affect the stability of the power system. The small targets such as pins in the unmanned aerial vehicle (UAV) inspection images of transmission lines occupy a small proportion in the images and the characteristic re...

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Bibliographic Details
Published inE3S Web of Conferences Vol. 300; p. 1011
Main Authors Wu, Jun, Cheng, Sheng, Pan, Shangzhi, Xin, Wei, Bai, Liangjun, Fan, Liang, Dong, Xiaohu
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 2021
Subjects
Algorithms
Counterbalances
Feature extraction
Image transmission
Inspection
Transfer learning
Transmission lines
Unmanned aerial vehicles
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Summary:Defects such as insulator, pins, and counterweight in highvoltage transmission lines affect the stability of the power system. The small targets such as pins in the unmanned aerial vehicle (UAV) inspection images of transmission lines occupy a small proportion in the images and the characteristic representations are poor which results a low defect detection rate and a high false positive rate. This paper proposed a transmission line pin defect detection algorithm based on improved Faster R-CNN. First, the pre-training weights with higher matching degree are obtained based on transfer learning. And it is applied to construct defect detection model. Then, the regional proposal network is used to extract features in the model. The results of defect detection are obtained by regression calculation and classification of regional characteristics. The experimental results show that the accuracy of the pin defect detection of the transmission line reaches 81.25%
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202130001011