Causal inference-guided deep learning method for vision-based defect detection of complex patterned fabrics

AI-based visual quality inspection is a pioneering scenario for the integration of two technologies and an important tool for complex product quality control. The typical research product in this paper is complex patterned fabrics, and a causal inference-guided deep learning method for vision-based defect detection is proposed to solve the detection challenges under complex background interference. First, a structural causal model for defect detection under complex background interference is constructed, and a causal intervention strategy to block the confounding effects caused by the background feature is proposed. Second, a defect feature-sensitive neural network (DFSNN) is established based on the causal intervention strategy, including two feature extraction modules that are fed with intact and defective fabric images from the same viewpoint, respectively. Then, during the training process, a causality-sensitive learning module is proposed, which differs the outputs of the two feature extraction modules and reconstructs the model loss function by maximizing the output difference so as to achieve the blocking of background features and the sensitivity learning of defective features. The experimental results show that DFSNN can effectively attenuate the confusion interference of background patterns and maintain 95% defect recognition accuracy. © 2023 Chinese Academy of Sciences. All rights reserved.