基于深度学习的纹理布匹瑕疵检测方法

Fabric defect detection is a key part of product quality assessment in the textile industry. Achieving fast, accurate and efficient fabric defect detection is of great significance for improving the productivity of the textile industry. In the production process of fabric, imbalance exists in the shape, size and quantity distribution of fabric defects, and the complex texture information of the jacquard fabric will cover the characteristics of the defect, which makes it difficult to detect fabric defects. This paper proposes a method for detecting defects in imbalanced texture fabric based on deep convolutional neural network (ITF-DCNN). First, an improved ResNet50 convolutional neural network model (ResNet50+) based on channel concatenate is established to optimize the fabric defect features. Second, F-FPN (filter-feature pyramid network) method for filtering redundant feature is proposed to filter the background features in the feature maps and enhance the semantic information of defect features. Finally, a MFL (multi focal loss) function weighted with the number of defects is construct to reduce the impact of imbalance on the model, and reduce the model＇s insensitivity to a small number of defects. Experiments shows the proposed method effectively improves the accuracy of fabric defect detection and the accuracy of defect positioning, while reducing the false detection rate and missed detection rate of defect detection, which is significantly higher than the mainstream fabric defect detection algorithm.