Improved YOLOv5s model for key components detection of power transmission lines.

2023-02-20

Mathematical biosciences and engineering : MBE

AI Summary

To address the low detection accuracy of small critical components (e.g., insulators, hardware) in complex backgrounds during intelligent inspection of high-voltage transmission lines, this paper proposes a lightweight and efficient YOLOv5s-based detection method. Specifically, we: (i) design an IoU-optimized k-means clustering distance metric for anchor box generation; (ii) incorporate the Convolutional Block Attention Module (CBAM) to enhance multi-scale feature discrimination; and (iii) adopt Focal Loss to mitigate class imbalance among component categories. Experimental results on a custom-built transmission line dataset show that the proposed model achieves 98.1% mAP, 97.5% precision, and 94.4% recall, with an inference speed of 84.8 FPS—significantly outperforming the baseline YOLOv5s. The method thus delivers both high accuracy and real-time performance, effectively supporting practical deployment in intelligent transmission line inspection systems.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Hardware-aware ML
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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 the detection accuracy and has advantages over other models in performance.

Problem

• Enhanced YOLOv5s for key components detection
• Improved accuracy in power grid inspection
• Addressing class imbalance with focal loss

Innovation

• Enhanced YOLOv5s anchor matching
• Incorporated CBAM attention mechanism
• Applied focal loss function