Currently, numerous automatic fabric defect detection algorithms have been proposed. Traditional machine vision algorithms that set separate parameters for different textures and defects rely on the manual design of corresponding features to complete the detection. To overcome the limitations of traditional algorithms, deep learning-based correlative algorithms can extract more complex image features and perform better in image classification and object detection. A pixel-level defect segmentation methodology using DeepLabv3+, a classical semantic segmentation network, is proposed in this paper. Based on ResNet-18, ResNet-50 and Mobilenetv2, three DeepLabv3+ networks are constructed, which are trained and tested from data sets produced by capturing or publicizing images. The experimental results show that the performance of three DeepLabv3+ networks is close to one another on the four indicators proposed (Precision, Recall, F1-score and Accuracy), proving them to achieve defect detection and semantic segmentation, which provide new ideas and technical support for fabric defect detection.

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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.

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