Research on refined UAV inspection method of wind/solar power stations based on YOLOv8 - Jieyi Pu; Qifeng Zhang; Wenbo Zhao; Wei Zhang; Zengren Qin; Yumeng Zhang (Journal of Engineering and Applied Science)
Abstract More and more research is focusing on the unmanned aerial vehicle (UAV) inspection of onshore wind/solar power stations; however, how to balance the contradiction between detection accuracy and efficiency is still a challenge for domestic and international researches. In this paper, for the sparse distribution of inspection targets in onshore wind/solar power stations, the YOLOv8 model is used to quickly extract information such as photovoltaic module contours and key points of wind turbine blades in the wide-angle image of the UAV. By connecting these key points in series, fine route planning of the UAV is realized, and the zoom lens is used to carry out efficient and fine inspection of photovoltaic (PV) modules and wind turbine blades. In order to accelerate the convergence of the PV module contour extraction model, this paper introduces a loss function for the difference in linear angular orientation, which improves the average IOU accuracy of the YOLOv8 model to 93%. In the extraction of key points such as the center and tip of wind turbine blades, this paper firstly adopts serpentine convolution to replace the traditional convolution operator in order to adapt the wind turbine blade features, and secondly incorporates the a priori information of the angle constraints between the blades into the loss function. Finally, multi-angle wind turbine imaging photos in desert, hill, rice field, and other scenes are quickly simulated by computer simulation software to improve the model generalization performance. The experimental results show that the improved model achieves 85.4% on the mAP50 metric, which is a 9.2% improvement over YOLOv8, and 6.2% on the more stringent mAP50:95 metric.