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| Multi-scale Corrugated Cardboards Defect Detection Algorithm Based on YOLOv8s |
| Received:February 28, 2025 |
| DOI：10.11980/j.issn.0254-508X.2025.06.021 |
| Key Words:YOLOv8s corrugated cardboards defect detection CCFM DySample up-sampling operator |
| Fund Project:湖北省科技服务人才项目（2023DJC199）。 |
| Author Name | Affiliation | Postcode | | TU Hua* | School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei Province, 430048 | 430048 | | JIANG Yajun* | School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei Province, 430048 | 430048 | | WU Wenlong | School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei Province, 430048 | 430048 | | HU Zhigang | School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei Province, 430048 | 430048 | | MA Ming | School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei Province, 430048 | 430048 | | WANG Zhenxin | School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan, Hubei Province, 430048 | 430048 |
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| Abstract:A multi-scale defect detection algorithm based on YOLOv8s was proposed to address the problems of inconspicuous features and easy missed detection of cross-scale defects such as water stain， scratch， crush， mechanical sabotage， and so on， in the detection of surface defects of corrugated cardboard. The neck structure of network was improved by cross-scale feature fusion module （CCFM）， combined with DySample up-sampling operator to enhance the sensitivity to cross-scale defects of scratches and low-resolution water stains， which effectively integrated the detailed features and contextual information and reduced the computational amount. The WIOUv3 loss function was improved to optimize the quality of anchor frames for fracking and mechanical breaking. The SPDConv module was introduced to retain the fine-grained features of the defects with inconspicuous features， which further improved the feature learning efficiency. The results showed that the improved model improved 1.3， 1.6 and 1.5 percentage points in precision， recall， and mean average precision， respectively， reduced the amount of parameters by 42.3%， improved the frame rate by 25.8%， significantly reduced the false-positive and false-negative cases， and improved the computing speed. The algorithm was able to realize efficient and accurate corrugated cardboard surface defect detection， which provided reliable technical support for corrugated cardboard production quality monitoring and had wide industrial application value. |
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