Color-patterned fabric defect detection algorithm based on triplet attention multi-scale U-shape denoising convolutional auto-encoder.

The scarcity of defect samples and the imbalance of defect types lead to the fact that achieving defect detection in color-patterned fabrics remains a challenge in the textile industry. Defect detection methods based on traditional auto-encoder are difficult to solve the problem of defect detection in complex color-patterned fabrics. In order to solve the problem of weak feature representation of traditional auto-encoders, this paper proposes an unsupervised method based on triplet attention multi-scale U-shape denoising convolutional auto-encoder (TA_MSUDCAE). The method further enhances the feature representation capability of the auto-encoder by introducing a triplet attention mechanism based on utilizing the multi-scale information of the image. Firstly, the defect-free samples that are added with Gaussian noise are used as inputs to the model in the training stage. The model is trained to reconstruct and repair the defective regions. Secondly, the test image is input to the trained model to obtain a normal reconstructed image, and the residual image is obtained by calculating the difference between the input image and the reconstructed image. Finally, the defect detection and localization results are obtained by threshold segmentation and mathematical morphology processing of the residual image. A large number of experiments have been carried out on a variety of representative color-patterned fabrics, and the results prove the effectiveness of the proposed method in fabric defect detection. [ABSTRACT FROM AUTHOR]