An insulator feature-enhanced segmentation method based on the residual-type attention mechanism

As an crucial component of power line, the timely check for the state of insulator is necessary for the normal running of transmission lines. In view of debasement of insulator segmentation accuracy in current transmission line caused by complex background, low contrast, and the quality of images is not guaranteed, we improve U-Net by combining attention mechanism and residual connection. Residual connection is added to the encoder part to improve the extraction of low-level semantic information, and the attention mechanism is added to the decoder part for integrating high and low level features better and reduce the error between them. We confirm the effectiveness of the improved module through experiments, while the results showing that the improved U-Net model segmentation performance on insulator dataset is improved from 0.875 to 0.912. And our method also outperforms previous segmentation work on insulator dataset.