基于YOLO的无人机电力线路杆塔巡检图像实时检测

无人机巡检已成为电力线路灾后巡检的重要方式。然而，目前的无人机巡检仍主要通过人工方式评估线路灾损，不仅费时费力，而且准确率低。提出了一种基于深度学习算法（YOLO）的实时目标检测模型，用于灾后根据无人机巡检视频实时检测电力杆塔的状态。通过对倒断类杆塔图像进行数据增广，解决了杆塔类别不平衡问题。通过使用K-means算法对杆塔数据集的目标框进行重新聚类，改进了YOLO算法参数。测试结果表明，该模型能有效检测多种环境下多种尺度的杆塔目标。改进后的模型在测试集上的召回率和交并比（IoU）较改进前有所提高，且平均均值精度（mAP）达到94.09%，检测速度达到20帧/s。此外，也对更快的简化版YOLO模型进行了测试，检测速度能达到30帧/s。

YOLO-Based Real-Time Detection of Power Line Poles from Unmanned Aerial Vehicle Inspection Vision

Unmanned aerial vehicles (UAV)-based inspection has become an important approach for power line inspection after disaster. However, the current UAV-based inspection is still performed manually for damage assessments, which is not only time-consuming but also poor in accuracy. In this paper a real-time detection model based on YOLO deep learning algorithm is presented to detect the status of power line poles automatically from the UAV vision data after disaster. The data augmentation is performed for collapsed towers to solve the class imbalance problem. To improve the parameters of YOLO, K-means algorithm is used to cluster object frames of pole data. The experimental results show that the proposed model can effectively detect multi-scale towers in multiple environments. The Recall and Intersection-over-Union (IoU) of the improved YOLO are improved, with the mean average precision (mAP) on the test set of 94.09% and the average processing speed of 20 frames per second (FPS) after improving the parameters. Moreover, we tested the simplified YOLO with faster speed, and the average processing speed reaches 30 FPS.