Study on surface defect detection algorithm of lightweight corrugated board
To address the problems of slow detection speed and low recognition accuracy in corrugated cardboard surface defect detection,YOLOv5s-GCS was proposed as a lightweight corrugated cardboard surface defect detection algorithm based on the improved YOLOv5s.The original Conv module in the YOLOv5s backbone network was replaced with the GhostConv module,the C3 module was replaced with the C2f module,and the Replacement Attention Mechanism(SA) module was integrated.SA module was introduced at the terminal of YOLOv5s neck network;and tests were conducted to validate the algorithm by constructing the corrugated cardboard surface defect dataset.The test results show that the average precision mean mAP,recall R,and precision P of YOLOv5s-GCS algorithm are 95.0％,89.2％,and 92.5％,respectively,which are 2.3％,1.3％,and 2.8％ higher than that of the original YOLOv5s.The detection speed reaches 19.9 fps,which is 5.7 fps higher than that of the original YOLOv5s.YOLOv5s-GCS algorithm is more conducive to carry out corrugated cardboard surface defect detection migration deployment and practical applications.The study can provide a reference for real-time detection in the field of surface defects.
国家科技期刊平台
NSTL
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