Fabric defect detection algorithm based on improved YOLOv5

• Published in: The Visual computer Vol. 40; no. 4; pp. 2309 - 2324
• Main Authors: Li, Feng, Xiao, Kang, Hu, Zhengpeng, Zhang, Guozheng
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Computer Graphics; Computer networks; Computer Science; Datasets; Deep learning; Defects; Frames per second; Image Processing and Computer Vision; Mathematical models; Model accuracy; Network management systems; Object recognition; Original Article; Parameters; Target detection; Teaching methods; Textile industry; Vision systems; Computer vision; Fabric defect detection; GHM loss; YOLOv5; SIoU loss

Fabric defect detection is an important part of the textile industry, aiming at the problems of many types of fabric defects, small size defects and unbalanced samples, an improved YOLOv5 fabric defect detection algorithm, FD-YOLOv5, was proposed. First, the coordinate attention module is embedded i...