Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m

2023-11-28

Applied Sciences

AI Summary

To address low detection accuracy in identifying foreign objects (e.g., balloons, kites, bird nests) on high-voltage transmission lines—caused by severe occlusion, large scale variations, and complex backgrounds—this paper proposes a lightweight and efficient detection method based on an improved YOLOv8m architecture. The method introduces three key innovations: (1) a Global Attention Mechanism (GAM) to enhance perception of occluded targets; (2) SPPCSPC replacing SPPF to improve multi-scale feature fusion efficiency; and (3) a Focal-EIoU loss function to mitigate positive–negative sample imbalance. Evaluated on a real-world dataset collected from Yunnan Power Grid, the proposed model achieves +2.7% mAP₀.₅, +4.0% mAP₀.₅:₀.₉₅, and +6.0% recall over the baseline. These improvements significantly enhance robustness and practicality for detecting small and occluded foreign objects under challenging field conditions.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search

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: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The safe operation of high-voltage transmission lines ensures the power grid’s security. Various foreign objects attached to the transmission lines, such as balloons, kites and nesting birds, can significantly affect the safe and stable operation of high-voltage transmission lines. With the advancement of computer vision technology, periodic automatic inspection of foreign objects is efficient and necessary. Existing detection methods have low accuracy because foreign objects attached to the transmission lines are complex, including occlusions, diverse object types, significant scale variations, and complex backgrounds. In response to the practical needs of the Yunnan Branch of China Southern Power Grid Co., Ltd., this paper proposes an improved YOLOv8m-based model for detecting foreign objects on transmission lines. Experiments are conducted on a dataset collected from Yunnan Power Grid. The proposed model enhances the original YOLOv8m by incorporating a Global Attention Module (GAM) into the backbone to focus on occluded foreign objects, replacing the SPPF module with the SPPCSPC module to augment the model’s multiscale feature extraction capability, and introducing the Focal-EIoU loss function to address the issue of high- and low-quality sample imbalances. These improvements accelerate model convergence and enhance detection accuracy. The experimental results demonstrate that our proposed model achieves a 2.7% increase in mAP_0.5, a 4% increase in mAP_0.5:0.95, and a 6% increase in recall.

Problem

• Detect foreign objects on high-voltage transmission lines
• Improve accuracy of object detection in complex scenarios
• Enhance YOLOv8m for better feature extraction and convergence

Innovation

• Incorporates Global Attention Module (GAM)
• Replaces SPPF with SPPCSPC module
• Introduces Focal-EIoU loss function