基于改进YOLOv3的输电线路部件实时检测

Real-time detection of transmission line components based on improved YOLOv3

针对基于深度学习的目标检测技术应用于工业领域无法在移动端嵌入式设备上实现高效且准确的检测这一问题,提出一种基于YOLOv3改进的输电线路部件实时检测算法轻量级特征融合检测模型LFF-DM(Lightweight Feature Fusion Detection Model)。一方面通过改进式的K-means算法得到聚类结果;另一方面结合深度可分离卷积和倒残差块设计出轻量化的网络结构。通过在自建的包含绝缘子、悬垂线夹、防震锤、鸟巢与导地线的专业巡检数据集上进行实验,结果表明在NVIDIA Jetson AGX Xavier设备上可以实现25 FPS的检测速度及90.48%mAP的检测精度,适用于输电线路移动端实时精确巡检。

Aiming at the problem that the object detection technology based on deep learning cannot be applied in the industrial field to realize efficient and accurate detection on mobile embedded devices,a lightweight feature fusion detection model(LFF-DM)for real-time detection of transmission line components based on improved YOLOv3 is proposed in this paper.On the one hand,the clustering results are obtained by the improved K-means algorithm;on the other hand,a lightweight network structure is designed by combining depth separable convolution and inverted residuals.Experiments are carried out on the self-built professional inspection data set including insulator,overhanging wire clip,anti-vibration hammer,birds nest,and ground guidewire,and the results show that the detection speed of 25 FPS and the detection accuracy of 90.48%mAP can be achieved on NVIDIA Jetson AGX Xavier equipment,which is suitable for real-time accurate inspection of transmission line mobile terminal.