DM-YOLO: transmission line fault detection based on dynamic multi-scale convolution and attention mechanism

Abstract To address the problem of low accuracy in transmission line fault detection caused by multi-scale targets faults in complex backgrounds, a novel approach named DM-YOLO is proposed. Firstly, to address the challenge of effectively extracting features from multi-scale targets faults, a dynamic multi-scale convolution module was designed and introduced into the original YOLOv8 network, enhancing the model’s ability to express features at different scales. Secondly, a multi-dimensional perceptual attention module was proposed and embedded into the feature extraction network, thus improving the detection accuracy by obtain the correlation and global information between different regions of the feature image. Thirdly, to address the problems of missing and false detection caused by the insufficient efficiency of fusing features at different levels, a multi-head feature fusion module was designed and introduced into the feature fusion network, which enhances the detection network’s comprehension of both semantic and textural information. Finally, to evaluate the algorithm’s performance, a dataset containing twelve types of fault samples was established, and comparative experiments were performed with other classic detection algorithms. The experimental results indicate that the enhanced model achieves an average accuracy of 93.8%, surpassing that of the original model. Furthermore, the proposed model demonstrates a high detection accuracy for multi-scale target faults within complex backgrounds.