A Light weight fabric defect detection model based on YOLOv5
On the basis of the low efficiency of manual detection of cloth defects and false detection,a fab-ric defect detection model G-YOLOv5 based on YOLOv5 algorithm is proposed.Firstly,Ghost module is a-dopted to replace the traditional convolution to reduce the amount of redundant parameters and calculation.Secondly,Coordinate Attention is added at the end of the backbone,strengthening the classification and po-sitioning performance of small target objects.Meanwhile,the lightweight up-sampling operator CARAFE is used to reduce the feature loss in the process of feature processing.The results show the mean average accura-cy of the improved algorithm on the fabric defect detection data set is 88.4％,which is 2.2 percentage points higher than that of YOLOv5 algorithm.And the amount of parameters is reduced to half of YOLOv5,which can achieve high detection accuracy in a small model and meet the detection needs of contemporary industry.
万方数据
维普
