卢志博,徐澄宇,杨罡,JUDE Michael Akotonou,张兴忠.基于改进YOLOv3的输电线路部件实时检测[J].电测与仪表,2023,60(7):138-144. LU Zhibo,XU Chengyu,YANG Gang,Jude Michael Akotonou,ZHANG Xinghzong.Real-time detection of transmission line components based on improved YOLOv3[J].Electrical Measurement & Instrumentation,2023,60(7):138-144.
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
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.
