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Nonwoven fabric defect detection is an essential part of the production process, and it is important to realize fast and accurate nonwoven fabric defect detection to improve production efficiency. Aiming at the problems of nonwoven fabric defect detection in which most defect targets are small and traditional industrial cameras are unable to recognize foreign impurities of the same color, a nonwoven fabric defect detection method based on hyperspectral imaging and improved YOLOv5 is proposed. Specifically, hyperspectral imaging technology is used to replace traditional vision and solve the problem of homochromatic foreign matter impurities from the dimension of spectrum. In addition, the LSK attention module is introduced into the YOLOv5 backbone network, which enables the network to learn the key feature information and enhance the detection of small targets. Finally, an improved light repetitive group frequency permutation network (Light-RepGFPN) is proposed in the neck structure, by which the feature fusion capability of the model is enhanced, so that the high-level semantic information and the low-level spatial information are fully fused to improve the detection accuracy. Combined with the LSK and Light-RepGFPN modules, we propose an improved YOLOv5 (LL-YOLOv5) defect detection network. It is experimentally proved that LL-YOLOv5 can achieve an average accuracy mean 90.3% in the nonwoven fabric hyperspectral image defects dataset, which is 2.2% higher than that of the original model, respectively.

Hyperspectral Imaging based Nonwoven Fabric Defect Detection Method using LL-YOLOv5