Multi-target Detection of Transmission Lines Based on Improved YOLOv5

Abstract

Abstract: In order to realize the identification and detection of transmission line components， a multi-target detection algorithm for transmission lines based on improved YOLOv5 is proposed for the current problems of deepening the number of target detection network layers， increasing the number of parameters and computation， resulting in poor real-time performance. Firstly， the number of parameters in the network was reduced by using the shuffleNetv2 structure as the backbone structure for network feature extraction. Secondly， the BottleneckCSP in the PANet network is changed to a Light_CSP module to speed up feature fusion. Thirdly， the CIoU loss， DIoU-NMS method is used to reduce the loss of position of the prediction frame and the problem of missed detection. Finally， in order to verify the effectiveness of the proposed algorithm， a transmission line image dataset was used for training and testing The results show that the improved YOLOv5 has a parametric count of 7.5×106， a floating point computation of 10.9， an average accuracy of 87.5% and an FPS of 69.2， which meets the requirements for accuracy， lightness and real-time inspection of transmission line components.

Key words: intelligent inspection, target detection, YOLOv5, transmission line

TANG Hao-wei, YAO Jun-cai, YAO Cong-ying, SUN Ying, PEI Xing-yi, SONG Chun-xiao. Multi-target Detection of Transmission Lines Based on Improved YOLOv5[J]. Computer and Modernization, 2023, 0(02): 78-82.

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