Surface Defect Detection of Highly Reflective Leather Based on Dual-Mask-Guided Deep-Learning Model

Automated defect detection of reflective product surface is a challenging task due to the appearance of specular highlight. Although various highlight removal methods have been proposed, they fail to distinguish bright areas from highlight, and may remove bright defects as specular highlights, which can affect the accuracy of subsequent defect detection. In this paper, we propose a dual-mask guided deep learning model for detecting surface defects on highly reflective leather, which can remove surface specular highlights in industrial product images while preserving bright defects. Specifically, we propose an image transformation network for joint specular highlight detection and removal. The network is composed of multiple large window attention and MLP blocks, which can capture multi-scale features effectively. In addition, we create a dataset with 522 reflective leather images and perform pixel-level annotation of both high-light regions and defect regions. We conduct experiments on the reflective leather dataset and public dataset to evaluate the performance of our method. Compared with other approaches, the results demonstrate the superiority of our proposed method.