Fabric Defect Detection Method Based on Improved U-Net
Rongqiang Liu, Minghui Li, Shi Jia-chen, Liang Yi-bin
Abstract
Abstract Computer vision builds a connection between image processing and industrials, bringing modern perception to the automated industrials. At the same time, defect detection based on deep learning has played an important role in automated detection. In this paper, an improved convolutional neural network CU-Net for fabric defect detection is proposed. In this method, the classical U-Net network was improved. On the basis of network size compression, attention mechanism is introduced and a new compound loss function is used for training. Using the public AITEX defect fabric data set as the test sample, the experimental result shows that the accuracy and recall of the proposed method are 98.3% and 92.7%, respectively. Compared with the highest scores of other detection methods, they are improved by 4.8% and 2.3%, which improves the detection accuracy of fabric defect significantly.