Bird Detection on Power Transmission Lines Based on Improved YOLOv7

Abstract

The safety of transmission lines is essential for ensuring the secure and dependable operation of the power grid. However, the harm caused by birds to transmission lines poses a direct threat to their safe operation. The main challenges in detecting birds on lines is that the detected targets are small, densely packed, and susceptible to environmental interference. We introduce a novel dynamic convolutional kernel specifically designed for detecting small and densely packed targets, the ODconv in the backbone of YOLOv7, to capture richer contextual information and improve performance. The substitution of Alpha_GIoU for CIoU in the original YOLOv7 network model serves to refine the loss function, decrease its parameters, and bolster the network’s resilience. The results confirmed that the proposed YOLOv7 with ODConv reached mAP0.5, mAP0.5:0.95, and precision of up to 78.42%, 46.14%, and 73.56% respectively. In contrast to the base model, the enhanced model demonstrated a 2.58% rise in mAP0.5, a 0.72% improvement in mAP0.5:0.95, and an increased precision of 2.34%.