Overhead Power Line Damage Detection: An Innovative Approach Using Enhanced YOLOv8

Abstract

This paper presents an enhanced version of YOLOv8 specifically designed for detecting damage in overhead power lines. Firstly, to improve the model’s robustness, an adaptive threshold mechanism is introduced that can dynamically adjust the detection threshold based on the brightness, contrast, and other characteristics of the input image. Secondly, a novel convolution method, GSConv, is adopted in the YOLOv8 framework, which balances the model’s running speed and accuracy. Finally, a lightweight network structure, Slim Neck, is introduced, effectively reducing the model’s complexity and computational load while maintaining good performance. These improvements enable our YOLOv8 model to achieve excellent performance in detecting ‘thunderbolt’ and ‘break’ types of cable damage. Experimental results show that the improved YOLOv8 network model has an average detection accuracy (mAP) of 90.2%, a recall rate of 91.6%, and a precision of 89.8% on the ‘Cable Damage Detection’ dataset from RoboFlow for ‘thunderbolt’. For ‘break’, the mAP is 86.5%, the recall rate is 84.1%, and the precision is 86.1%. Compared with the original YOLOv8 model, these indicators have been significantly improved, highlighting the high practical value and strong generalization ability of the proposed algorithm in detecting damage to overhead power lines. This also demonstrates the high practical value of the method in future research directions.

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