Improved YOLOv5 foreign object detection for transmission lines
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Abstract:
The traditional transmission line detection has the problems of low efficiency. For improve the performance of transmission line foreign object detection, this paper proposes an improved YOLOv5 transmission line foreign object detection algorithm. First, efficient channel attention (ECA) attention module is introduced in the backbone network for focusing the target features and improving the feature extraction capability of the network. Secondly, bilinear interpolation upsampling is introduced in the neck network to improve the model detection accuracy. Finally, by integrating the EIoU loss function and Soft non-maximum suppression (Soft NMS) algorithm, the convergence speed of the model is accelerated while the detection effect of the model is enhanced. Relative to the original algorithm, the improved algorithm reduces the number of parameters by 16.4%, increases the mean average precision(mAP)@0.5 by 3.9%, mAP@0.5:0.95 by 6.3%, and increases the detection speed to 55.3FPS.The improved algorithm is able to improve the performance of the foreign object detection in transmission lines effectively.
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Project Supported:
Tianjin Graduate Student Research and Innovation Program
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