Improved YOLOv5s model for key components detection of power transmission lines

TL;DR

This paper presents an improved YOLOv5s-based model for detecting key components of power transmission lines, enhancing accuracy and efficiency for intelligent inspection tasks in challenging environments.

Contribution

The study introduces modifications to YOLOv5s, including adjusted anchor matching, CBAM attention, and focal loss, achieving higher detection accuracy for transmission line components.

Findings

mAP reached 98.1%

Precision reached 97.5%

Detection speed was 84.8 FPS

Abstract

High-voltage transmission lines are located far from the road, resulting in inconvenient inspection work and rising maintenance costs. Intelligent inspection of power transmission lines has become increasingly important. However, subsequent intelligent inspection relies on accurately detecting various key components. Due to the low detection accuracy of key components in transmission line image inspection, this paper proposed an improved object detection model based on the YOLOv5s (You Only Look Once Version 5 Small) model to improve the detection accuracy of key components of transmission lines. According to the characteristics of the power grid inspection image, we first modify the distance measurement in the k-means clustering to improve the anchor matching of the YOLOv5s model. Then, we add the convolutional block attention module (CBAM) attention mechanism to the backbone network to improve accuracy. Finally, we apply the focal loss function to reduce the impact of class imbalance. Our improved method's mAP (mean average precision) reached 98.1%, the precision reached 97.5%, the recall reached 94.4%, and the detection rate reached 84.8 FPS (frames per second). The experimental results show that our improved model improves detection accuracy and has performance advantages over other models.