Identification and Positioning of Engineering Vehicle in UAV Inspection for Optical Cable Lines

In order to overcome the low efficiency and high risk of the traditional manual inspection method of optical cable lines, this paper proposes a method of identification and positioning of engineering vehicle in UAV inspection. The method is based on the Pytroch deep learning framework and uses YOLOv4 target detection algorithm to realize intelligent detection of hidden faults. At the same time, the paper designs the positioning algorithm of the UAV inspection target based on the image coordinates of the recognition target and the multiple coordinate conversion method. Through test experiments, the results show that the AP value of engineering vehicles inspected by UAV reaches 83.28%, and the target positioning accuracy is about 20 meters, which can meet the inspection requirements. The test results show that based on the method proposed in the paper, the hidden faults of the optical cable can be accurately identified and located, which can effectively improve the inspection efficiency and reduce the workload of the inspectors.