Improved U-Net based insulator image segmentation method based on attention mechanism

To realize the accurate identification and segmentation of the insulator string in the complex background image with diverse appearance and obscuration, this paper proposes an insulator segmentation method based on improved U-Net. The algorithm embeds the attention mechanism ECA-Net (Efficient Channel Attention Neural Networks) in the coding stage of U-Net to improve the model's ability to extract semantic features, thereby improving the accuracy of insulator detection. Experimental results show that the average overlap IOU of the proposed method is 96.8%, which can more accurately segment different types of insulators in complex backgrounds. (C) 2021 The Author(s). Published by Elsevier Ltd.

Automated summary

This paper proposes an insulator segmentation method based on improved U-Net, which embeds the attention mechanism ECA-Net to enhance the model's ability in extracting semantic features and improving the accuracy of insulator detection. The experimental results demonstrate that the proposed method achieves an average overlap IOU of 96.8%, enabling more accurate segmentation of different types of insulators in complex backgrounds.