Monocular Distance Measurement Method for Insulator in Unmanned Aerial Vehicle Inspection Images Based on YOLOv5 Algorithm
In order to enhance the security of UAV flights and improve line operation and maintenance capabilities,a UAV monocular ranging method is proposed for implementing an accurate auxiliary distance measurement function based on the YOLOv5 deep learning algorithm.The existing open source insulator dataset is expanded and calibrated,then the YOLOv5s model is trained,validated and tested to build a distance measurement model for insulator strings,and the design code is added to the detection module to realize the identification and ranging of insulators.The experimental results show that the model can accurately identify different types of insulators on transmission lines and perform accurate ranging,with an average ranging error of 4.76％;the model is most effective in identifying and ranging composite insulators,and the maximum ranging error is 6％when changing the pitch angle and relative distance;the greater the change in brightness under different weather conditions,the greater the error in identifying and ranging insulators,when the brightness increases to 100％,the error can reach up to 12.3％.The average time required for distance measurement is 0.298 4 s.The proposed method enables efficient and highly accurate distance measurement and provides support for safe distance measurement for UAV inspection.
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