An Improved YOLOv3 for Foreign Objects Detection of Transmission Lines

Abstract

The surrounding of transmission lines is complex, large-scale construction machinery, and smoke and fire pose threats to the circuit facilities and wires. When anomaly occurs, much time is required for the State Grid Corporation to fix it manually. To reduce the inspection burden, we propose a lightweight model running on embedded device to detect foreign objects of transmission lines. Based on the You Only Look Once (YOLO) v3, we use Mobilenetv2 instead of Darknet-53 as the backbone, and use depthwise separable convolution to replace 3 x 3 convolutional kernels in detection head, which greatly reduces the parameter size of the network. And the Fully Convolutional One-Stage Object Detection (FCOS)-like encoding and decode scheme is adopted to reduce network complexity. Meanwhile, in order to compensate for the degradation of accuracy, we have improved data augmentation, learning rate, and loss function. The experiments show that compared with other existed models, the improved YOLOv3 model has a smaller model size and higher detection speed without notably reducing detection accuracy, which has achieved the balance between detection speed and accuracy.