MRA-YOLOv8: A Transmission Line Fault Detection Algorithm Integrating Multi-Scale Feature Fusion

Aiming at the problems of complex background interference and partial occlusion of fault targets during UAV transmission line inspection, this paper proposes an MRA-YOLOv8-based fault detection method for transmission line components. Firstly, the YOLOv8 network is adopted as the baseline framework, and a self-attention mechanism is incorporated into its backbone network to enhance the detection accuracy for occluded targets. Subsequently, a Multi-scale Attention Aggregation module is introduced into the neck network to improve the feature extraction capability for fault targets against complex backgrounds. Furthermore, the bounding box loss function is optimized to mitigate class imbalance issues, thereby boosting the model's fault detection performance. Finally, the proposed algorithm is validated using inspection data collected over the past three years from an electric power inspection department. Experimental results demonstrate that the proposed method achieves an average detection precision of 92.5% and a recall rate of 90.9%.