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| Surface Defects Identification Method for Reconstituted Tobacco Based on Lightweight Object Detection Model |
| Received:January 20, 2026 Revised:February 06, 2026 |
| DOI：10.11980/j.issn.0254-508X.2026.05.023 |
| Key Words:reconstituted tobacco defect detection deep learning dual-attention mechanism lightweight model |
| Fund Project:湖北省自然科学基金面上项目（2023AFB878）；湖北省自然科学基金青年项目（2024AFB259）。 |
| Author Name | Affiliation | Postcode | | WANG Shuiming* | 1China Tobacco Hubei Industrial Co., Ltd., Wuhan, Hubei Province, 430040
2Hubei Xinye Reconstituted Tobacco Development Co., Ltd., Wuhan, Hubei Province, 430056 | 430056 | | LI Pengfei* | 1China Tobacco Hubei Industrial Co., Ltd., Wuhan, Hubei Province, 430040
2Hubei Xinye Reconstituted Tobacco Development Co., Ltd., Wuhan, Hubei Province, 430056 | 430056 | | LIN Jie | 1China Tobacco Hubei Industrial Co., Ltd., Wuhan, Hubei Province, 430040
2Hubei Xinye Reconstituted Tobacco Development Co., Ltd., Wuhan, Hubei Province, 430056 | 430056 | | LI Yichen | 1China Tobacco Hubei Industrial Co., Ltd., Wuhan, Hubei Province, 430040
2Hubei Xinye Reconstituted Tobacco Development Co., Ltd., Wuhan, Hubei Province, 430056 | 430056 | | TANG Tianming | 1China Tobacco Hubei Industrial Co., Ltd., Wuhan, Hubei Province, 430040
2Hubei Xinye Reconstituted Tobacco Development Co., Ltd., Wuhan, Hubei Province, 430056 | 430056 | | CHEN Qianjin | 1China Tobacco Hubei Industrial Co., Ltd., Wuhan, Hubei Province, 430040
2Hubei Xinye Reconstituted Tobacco Development Co., Ltd., Wuhan, Hubei Province, 430056 | 430056 | | WANG Han | 3School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, Hubei Province, 430200 | 430200 | | WEI Ningfeng | 3School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, Hubei Province, 430200 | 430200 |
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| Abstract:To address the issues of low efficiency， high false detection， and difficult localization in manual inspection of surface defects in paper-making reconstituted tobacco， this paper proposed an automatic defect recognition method based on a lightweight object detection model. First， a defect detection platform based on dual-line-scan cameras and auxiliary lighting was constructed to capture defect images. Subsequently， batch image preprocessing and data augmentation methods were proposed to expand and optimize the dataset. A combined manual and automatic annotation approach was adopted to perform multi-category labeling according to defect characteristics， obtaining sufficient high-precision training data. Finally， a lightweight object detection model named YOLO-D was developed， incorporating a dual-attention mechanism and a dual-detection-head structure. The results showed that the model achieved a precision of 87.7% with a computational load of only 20.8 GFLOPs. Compared to the YOLOv8 model， precision was improved by 2.2% and computational load was reduced by 26.8%. The proposed method enabled fast and accurate identification of surface defects in reconstituted tobaccos. |
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