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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 Name | Affiliation | Postcode | | 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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