关键词: CenterNet, 深层特征融合, 电力巡检, 故障检测, 实时检测

Abstract:

As universal deep learning object detection technology is difficult to achieve real-time and efficient fault detction during high-voltage transmission and transmission line inspection, a real-time detection method is proposed for inspecting the fault of high-voltage transmission line based on CenterNet improved algorithm. The three kinds of common inspection faults such as insulator self-explosion, shock hammer falling off and bird’s nest are detected. An efficient DLA-SE deep feature extraction network is designed based on Deep Layer Aggregation（DLAnet）, Squeeze-and-Excitation modules, and deformable convolution. In CenterNet architecture, as heat map of center point, width height and offset information of the object are obtained through the feature extraction network of DLA-SE, which gets the object bounding box. Experimental results show that under the nvidia GTX 1080 test conditions, the proposed method has a mAP of 0.917 and an inference speed of 27.03 frame/s in comparison with CenterNet, SSD and YOLOv3 method, and detection accuracy has been greatly improved, which proves the method effectiveness.

Key words: CenterNet, Deep Layer Aggregation（DLAnet）, electric power inspection, fault detection, real-time detection