| Abstract: | The background pattern of patterned fabrics is complex, which has a great interference in the extraction of defect features. Traditional machine vision algorithms rely on artificially designed features, which are greatly affected by background patterns and are difficult to effectively extract flaw features. Therefore, a convolutional neural network(CNN) with automatic feature extraction is proposed. On the basis of the two-stage detection model Faster R-CNN, Resnet-50 is used as the backbone network, and the problem of flaws with extreme aspect ratio is solved by improving the initialization algorithm of the prior frame aspect ratio, and the improved multi-scale model is designed to improve detection of small defects. The cascade R-CNN is introduced to improve the accuracy of defect detection, and the online hard example mining(OHEM) algorithm is used to strengthen the learning of hard samples to reduce the interference of complex backgrounds on the defect detection of patterned fabrics, and construct the focal loss as a loss function to reduce the impact of sample imbalance. In order to verify the effectiveness of the improved algorithm, a defect detection comparison experiment was set up. The experimental results show that the accuracy of the defect detection algorithm of patterned fabrics in this paper can reach 95.7%, and it can accurately locate the defect location and meet the actual needs of the factory. |
