Colour‐patterned fabric defect detection based on an unsupervised multi‐scale U‐shaped denoising convolutional autoencoder model

Abstract

AbstractThis study proposes an unsupervised, learning‐based, reconstructed scheme and a residual analysis‐based defect detection model for colour‐patterned fabric defect detection problems in the clothing process industry. It solves the challenging problems of existing supervised fabric defect detection methods, such as high costs in manually labelling samples and designing features, unstable generalisation ability and scarcity of defective samples. First, for a specific texture, the training set was constructed by collecting easily accessible defect‐free colour‐patterned fabric images. Second, a multi‐scale U‐shaped denoising convolutional autoencoder was modelled using defect‐free samples, which can reconstruct the newly tested colour‐patterned fabric images automatically. Subsequently, a residual map between the original image and corresponding reconstructed image was calculated. Finally, the defective areas were detected and accurately localised by further opening operations. The experimental results indicated that the proposed method is valid and robust for detecting defects in various colour‐patterned fabrics. Moreover, with the YDFID‐1 dataset, compared with other models, the intersection over union index of the model proposed in the current paper was improved by at least 3.95%.