Research on Defect Detection for Overhead Transmission Lines Based on the ABG-YOLOv8n Model

Yang Yu, Hongfang Lv (), Wei Chen and Yi Wang
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Yang Yu: School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China
Hongfang Lv: School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China
Wei Chen: School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China
Yi Wang: School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China

Energies, 2024, vol. 17, issue 23, 1-12

Abstract: In the field of smart grid monitoring, real-time defect detection for overhead transmission lines is crucial for ensuring the safety and stability of power systems. This paper proposes a defect detection model for overhead transmission lines based on an improved YOLOv8n model, named ABG-YOLOv8n. The model incorporates four key improvements: Lightweight convolutional neural networks and spatial–channel reconstructed convolutional modules are integrated into the backbone network and feature fusion network, respectively. A bidirectional feature pyramid network is employed to achieve multi-scale feature fusion, and the ASFF mechanism is used to enhance the sensitivity of YOLOv8n’s detection head. Finally, comprehensive comparative experiments were conducted with multiple models to validate the effectiveness of the proposed method based on the obtained prediction curves and various performance metrics. The validation results indicate that the proposed ABG-YOLOv8n model achieves a 4.5% improvement in mean average precision compared to the original YOLOv8n model, with corresponding increases of 3.6% in accuracy and 2.0% in recall. Additionally, the ABG-YOLOv8n model demonstrates superior detection performance compared to other enhanced YOLO models.

Keywords: lightweighting; bidirectional paths; spatial and channel reconstruction; cross-scale feature fusion; ASFF mechanism (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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