基于YOLOv4的输电线路外破隐患识别算法

针对人工巡检及传统视频监测方式不能及时识别输电线路外破隐患的问题, 本文提出基于YOLOv4的输电线路外破隐患识别算法. 该算法采用改进K-means算法对图片样本集目标的大小进行聚类分析, 筛选出符合检测目标特征的锚框, 之后利用CSPDarknet-53残差网络提取图片深层次网络特征数据, 并采用SPP算法对特征图进行处理增加感受野, 提取更高层次的语义特征. 最后结合实际的输电线路现场监控图片, 测试结果表明该算法能够及时准确检测到外破隐患.

Identification Algorithm of Transmission Line External Hidden Danger Based on YOLOv4

In this paper, we propose an algorithm based on YOLOv4 to solve the problem that manual inspection and traditional video monitoring methods cannot identify the external hidden dangers of transmission lines in time. In this algorithm, cluster analysis is performed with the improved K-means algorithm on the size of the targets in the image sample set to select the anchor frames that conform to the characteristics of detection targets. After that, the CSPDarknet-53 residual network is used to extract the deep-seated network feature data of the images, and the feature map is processed by the SPP algorithm to increase the receptive field and extract higher-level semantic features. Finally, in combination with the monitoring pictures of transmission lines, the test results show that the proposed algorithm can detect external hidden dangers timely and accurately.