基于无人机机载AI模块的电力巡检技术

Power inspection technology based on UAV airborne AI module

针对输电线路无人机巡检实时性和准确性的要求,深入研究了YOLOv3目标检测算法在无人机巡检机载AI模块中的应用。利用将目标检测候选区选取和对象识别合二为一的YOLOv3算法,结合多尺度特征融合方式实现了目标检测的高准确性和实时优化,并采用残差块解决了模型退化问题。输电线路绝缘子检测结果表明:YOLOv3算法平均精度可达90%,相同条件下YOLOv3算法平均处理速度约为Faster RCNN算法的3.2倍,约为SSD算法的1.6倍。

According to the requirements for instantaneity and accuracy by UAV inspection of transmission line,the application of YOLOv3 target detection algorithm in UAV inspection airborne AI module was deeply studied.Through the YOLOv3 algorithm for both object detection candidate region selection and object recognition,in combination with the multi-scale feature fusion method,the high accuracy and instantaneity optimization of target detection were realized,and the residual block was used to solve the problem of model degradation.The results of transmission line insulator detection show that the average accuracy of YOLOv3 algorithm can reach 90%.Under the same conditions,the average processing speed of YOLOv3 algorithm is 3.2 times that of Faster RCNN algorithm and 1.6 times that of SSD algorithm.