Accurate recognition of micromorphology images of epoxy coatings for deep-sea environments based on a deep learning super-resolution method

Abstract

Crack initiation and extension occur in organic coatings during service in deep-sea environments. However, when extracting detailed crack information from SEM images of epoxy mica coatings at different time periods in a simulated deep-sea fluid-hydraulic environment, uninteresting background regions are treated equally, resulting in unnecessary computational redundancy. To address the relatively blurred edges and unclear textures of SEM images, a crack image super-resolution network based on global mixed attention (GMA-net) is proposed for application to SEM images of organic coatings. The recognition results of the images processed with GMA-net were compared with those of the original images and the images processed with bicubic method, respectively. The results show that this method not only refrains from destroying the clarity of the original images but also greatly outperforms bicubic method in terms of precision, recall, mAP50 and mPA50–95, which are improved by approximately 23.1 %, 32.4 %, 36.4 % and 26.7 %, respectively. This method effectively highlights the details and improves the recognition accuracy of the edge texture with the aim of providing a good basis for subsequent recognition and even lifetime prediction studies.