Abstract
To improve the detection rate of defect and the fabric product quality, a higher real-time performance fabric defect detection method based on the improved YOLOv3 model is proposed. There are two key steps: first, on the basis of YOLOv3, the dimension clustering of target frames is carried out by combining the fabric defect size and k-means algorithm to determine the number and size of prior frames. Second, the low-level features are combined with the high-level information, and the YOLO detection layer is added on to the feature maps of different sizes, so that it can be better applied to the defect detection of the gray cloth and the lattice fabric. The error detection rate of the improved network model is less than 5% for both gray cloth and checked cloth. Experimental results show that the proposed method can detect and mark fabric defects more effectively than YOLOv3, and effectively reduce the error detection rate.
Highlights
Traditional defect detection mainly relies on experienced professionals
Based on the above research, we apply the convolutional neural network (CNN) to textile companies to solve the problem of fabric defect detection
Using the gray cloth data as the experimental object, the priori frame of YOLOv3 network is modified according to clustering results, and the experiment was compared with the YOLOv3 and the improved network model
Summary
Traditional defect detection mainly relies on experienced professionals. It has a certain subjectivity and depends on the personal experience of the inspectors. Based on the good performance of YOLOv3, many researchers have introduced the network model into their own research field and achieved good results.18–20 Based on the above research, we apply the CNN to textile companies to solve the problem of fabric defect detection. We combine the image pyramid to obtain the different scales of feature maps and add the detection layer to improve the network structure.
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