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12 November 2024 Research on unmanned aerial vehicle power inspection technology based on YOLOV3
Jie Liu, Weimo Lu, Zhikun Wang, Dong Xie, Hongbo Shi
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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 133951H (2024) https://doi.org/10.1117/12.3050112
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
In order to meet the requirements of real-time and accuracy for unmanned aerial vehicle (UAV) inspection of transmission lines, this paper deeply studies the application of YOLOV3 object detection algorithm in the onboard AI module of UAV inspection. By integrating the target detection candidate region selection and object recognition into one, the YOLOV3 algorithm, combined with multi-scale feature fusion, realizes high accuracy and real-time optimization of target detection and uses residual blocks to solve the problem of model degradation. The test results of transmission line insulators show that the average accuracy of YOLOV3 algorithm can reach 90%. Under the same conditions, the average processing speed of YOLOV3 algorithm is about 3.2 times that of Faster RCNN algorithm.
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Jie Liu, Weimo Lu, Zhikun Wang, Dong Xie, and Hongbo Shi "Research on unmanned aerial vehicle power inspection technology based on YOLOV3", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 133951H (12 November 2024); https://doi.org/10.1117/12.3050112
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KEYWORDS
Object detection
Detection and tracking algorithms
Evolutionary algorithms
Inspection
Target detection
Unmanned aerial vehicles
Dielectrics
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