Fabric defect detection using cartoon-texture image decomposition model and visual saliency method

Fabric defect detection is of great importance in the modern textile industry. In actual production, subjective factors usually affect manual detection, which is prone to problems such as false and missed detection. With the development of computer vision, a large number of fabric defect automatic detection algorithms have been proposed. Traditional algorithms rely too heavily on setting parameters manually, while deep learning algorithms have expensive training and computing costs. Given these limitations, a new fabric defect detection method based on the latest cartoon texture image decomposition model, visual saliency algorithm, and mathematical morphology, is proposed in this article. A digital image acquisition system is also designed and constructed. Therefore, the self-made dataset used in the experiment is composed of self-collected images and network public images. To further evaluate the performance of the proposed method, this study conducted fabric defect detection experiments and comparison experiments based on the self-made dataset. The results show that this method can successfully detect fabric defects, having high detection accuracy and efficiency. Combining subjective vision and objective evaluation, comparison experiments prove that this method is superior to other common fabric defect detection methods and has the highest value of accuracy and F1-score. This research provides a new method and technical support for fabric defect detection and other fields.

Automated summary

This article proposes a new fabric defect detection method based on the latest cartoon texture image decomposition model, visual saliency algorithm, and mathematical morphology. Experiments conducted using a self-made dataset and comparison with other common fabric defect detection methods show that this method has high detection accuracy and efficiency, and outperforms other methods in subjective vision and objective evaluation.