Lightweight fabric defect detection algorithm based on YOLOv5n
To address the issue of low accuracy in detecting fabric defects using lightweight models,a fabric defect detection algorithm based on YOLOv5n was proposed,which combines context enhancement and mixed receptive fields.Firstly,a module of Spatial Pyramid for Lightweight Atrous was designed for the backbone network,and the down-sampling ratio of the backbone network was increased to 64,enhancing contextual information while extracting deeper semantic information to improve the recognition performance of the model;Secondly,a hybrid receptive field fusion module was designed for the neck network to replace the original C3 module for feature fusion,improving the detection accuracy of extreme aspect ratio targets.The experiment shows that the mAP50,Precision,and Recall of this algorithm on the Tianchi fabric dataset have reached 93.1%,91.6%,and 89.1%,respectively.Compared with the original YOLOv5n,it has improved by 4.9%,7.3%,and 5.0%,and the model file size is only 6.28 MB,making it more suitable for the field of fabric defect detection.
defects detectiondeep learningYOLOv5nspatial pyramidreceptive field fusion
万方数据
维普
