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Surface Defect Detection Method for Corrugated Box for Cigarettes Based on YOLO v3 Algorithm
Received:November 22, 2022  
DOI：10.11980/j.issn.0254-508X.2023.06.017
Key Words:cigarette corrugated box recycling  defect detection  YOLO v3
Author NameAffiliationPostcode
JIA Weiping China Tobacco Hubei Industry Co.， Ltd.， Wuhan， Hubei Province， 430000 430000
CHU Wei China Tobacco Hubei Industry Co.， Ltd.， Wuhan， Hubei Province， 430000 430000
LIU Wenting China Tobacco Hubei Industry Co.， Ltd.， Wuhan， Hubei Province， 430000 430000
HUANG Ke China Tobacco Hubei Industry Co.， Ltd.， Wuhan， Hubei Province， 430000 430000
LI Chenqiao China Tobacco Hubei Industry Co.， Ltd.， Wuhan， Hubei Province， 430000 430000
WU Fei* Wuhan University of Technology， Wuhan， Hubei Province， 430070 430070
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Abstract:A detection algorithm based on YOLO v3 was proposed， and an evaluation system for the recycling performance of corrugated box for cigarettes was established. The recognition results of surface defects of collected typical corrugated box for cigarettes using Faster RCNN and YOLO v3 deep neural network target detection algorithms were compared. Based on the OpenCV library and Canny algorithm， a reasonable solution suitable for measuring the sizes and distribution locations of surface defects of corrugated box for cigarettes was developed. The results showed that the algorithm had an average accuracy of 92.23% and could successfully achieve the detection of defect locations and size.
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