CP-YOLO: An Algorithm for Cigarette Pack Defects Detection Based on CCD Images
In cigarette production, detecting cigarette pack defects is crucial for ensuring that products meet quality standards. The failure to detect defective packs promptly may affect production efficiency and material consumption. Hence, in this study, we used Charge-Coupled Device (CCD) cameras to colle...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10942327/ |
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| _version_ | 1850271888778985472 |
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| author | Peng Dong Weihua Feng Rui Wang Mingyan Zhang Qunye Hong Yongsheng Wang Di Wang Guohao Zong |
| author_facet | Peng Dong Weihua Feng Rui Wang Mingyan Zhang Qunye Hong Yongsheng Wang Di Wang Guohao Zong |
| author_sort | Peng Dong |
| collection | DOAJ |
| description | In cigarette production, detecting cigarette pack defects is crucial for ensuring that products meet quality standards. The failure to detect defective packs promptly may affect production efficiency and material consumption. Hence, in this study, we used Charge-Coupled Device (CCD) cameras to collect many defect images from real industrial production lines. A relatively comprehensive cigarette pack defect dataset, called CigPack, was established based on complex defect features of different sizes and structures. Consequently, this paper proposes an improved CP-YOLO algorithm based on YOLOv5 to address the characteristics of cigarette pack defects including large size variations and complex foreground-background information. The algorithm integrates multi-scale aggregate convolution into the BottleneckCSP architecture to form the C3MSAC module. This module extracts and fuses grouped features at multiple scales to enhance the multi-scale representation of input feature maps. Additionally, a balanced-domain loss function, which introduces normalized Wasserstein distance to optimize the guidance of the network for bounding boxes with different geometric characteristics, is proposed. Experimental results demonstrate that CP-YOLO achieved average accuracy improvements of 2.9% and 2.5% on the CigPack dataset, without increasing computational complexity. The model also demonstrated excellent detection accuracy and robustness on steel surface defect dataset and printed circuit board defect dataset. |
| format | Article |
| id | doaj-art-3bb6f1f96c454ba4b0ac2c07e7e0ac58 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3bb6f1f96c454ba4b0ac2c07e7e0ac582025-08-20T01:52:03ZengIEEEIEEE Access2169-35362025-01-0113573545736810.1109/ACCESS.2025.355498910942327CP-YOLO: An Algorithm for Cigarette Pack Defects Detection Based on CCD ImagesPeng Dong0https://orcid.org/0000-0002-9338-7704Weihua Feng1Rui Wang2Mingyan Zhang3Qunye Hong4Yongsheng Wang5Di Wang6Guohao Zong7https://orcid.org/0000-0002-0617-3610Zhengzhou Tobacco Research, China National Tobacco Corporation (CNTC), Zhengzhou, ChinaZhengzhou Tobacco Research, China National Tobacco Corporation (CNTC), Zhengzhou, ChinaZhengzhou Tobacco Research, China National Tobacco Corporation (CNTC), Zhengzhou, ChinaGolden Leaf Production and Manufacturing Center, Zhengzhou, ChinaZhengzhou Tobacco Research, China National Tobacco Corporation (CNTC), Zhengzhou, ChinaZhengzhou Tobacco Research, China National Tobacco Corporation (CNTC), Zhengzhou, ChinaZhengzhou Tobacco Research, China National Tobacco Corporation (CNTC), Zhengzhou, ChinaZhengzhou Tobacco Research, China National Tobacco Corporation (CNTC), Zhengzhou, ChinaIn cigarette production, detecting cigarette pack defects is crucial for ensuring that products meet quality standards. The failure to detect defective packs promptly may affect production efficiency and material consumption. Hence, in this study, we used Charge-Coupled Device (CCD) cameras to collect many defect images from real industrial production lines. A relatively comprehensive cigarette pack defect dataset, called CigPack, was established based on complex defect features of different sizes and structures. Consequently, this paper proposes an improved CP-YOLO algorithm based on YOLOv5 to address the characteristics of cigarette pack defects including large size variations and complex foreground-background information. The algorithm integrates multi-scale aggregate convolution into the BottleneckCSP architecture to form the C3MSAC module. This module extracts and fuses grouped features at multiple scales to enhance the multi-scale representation of input feature maps. Additionally, a balanced-domain loss function, which introduces normalized Wasserstein distance to optimize the guidance of the network for bounding boxes with different geometric characteristics, is proposed. Experimental results demonstrate that CP-YOLO achieved average accuracy improvements of 2.9% and 2.5% on the CigPack dataset, without increasing computational complexity. The model also demonstrated excellent detection accuracy and robustness on steel surface defect dataset and printed circuit board defect dataset.https://ieeexplore.ieee.org/document/10942327/YOLOv5cigarette packdefect detectionmulti-scale feature extractionWasserstein distance |
| spellingShingle | Peng Dong Weihua Feng Rui Wang Mingyan Zhang Qunye Hong Yongsheng Wang Di Wang Guohao Zong CP-YOLO: An Algorithm for Cigarette Pack Defects Detection Based on CCD Images IEEE Access YOLOv5 cigarette pack defect detection multi-scale feature extraction Wasserstein distance |
| title | CP-YOLO: An Algorithm for Cigarette Pack Defects Detection Based on CCD Images |
| title_full | CP-YOLO: An Algorithm for Cigarette Pack Defects Detection Based on CCD Images |
| title_fullStr | CP-YOLO: An Algorithm for Cigarette Pack Defects Detection Based on CCD Images |
| title_full_unstemmed | CP-YOLO: An Algorithm for Cigarette Pack Defects Detection Based on CCD Images |
| title_short | CP-YOLO: An Algorithm for Cigarette Pack Defects Detection Based on CCD Images |
| title_sort | cp yolo an algorithm for cigarette pack defects detection based on ccd images |
| topic | YOLOv5 cigarette pack defect detection multi-scale feature extraction Wasserstein distance |
| url | https://ieeexplore.ieee.org/document/10942327/ |
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