Affiliation:
1. Xi’an Polytechnic University, Xi’an, China
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.
Funder
scientific research and sharing platform construction project of shaanxi province
National Natural Science Foundation of China
the Key Research and Development Program of Shaanxi Province
shaanxi university of science and technology
Shaanxi Provincial Association of Science and Technology Young Talents Promotion Program
xi’an polytechnic university
Subject
General Materials Science
Cited by
76 articles.
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