Research on a Multi-Type Barcode Defect Detection Model Based on Machine Vision
Barcodes are ubiquitous in manufacturing and logistics, but defects can reduce decoding efficiency and disrupt the supply chain. Existing studies primarily focus on a single barcode type or rely on small-scale datasets, limiting generalizability. We propose Y8-LiBAR Net, a lightweight two-stage fram...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8176 |
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| author | Ganglong Duan Shaoyang Zhang Yanying Shang Yongcheng Shao Yuqi Han |
| author_facet | Ganglong Duan Shaoyang Zhang Yanying Shang Yongcheng Shao Yuqi Han |
| author_sort | Ganglong Duan |
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| description | Barcodes are ubiquitous in manufacturing and logistics, but defects can reduce decoding efficiency and disrupt the supply chain. Existing studies primarily focus on a single barcode type or rely on small-scale datasets, limiting generalizability. We propose Y8-LiBAR Net, a lightweight two-stage framework for multi-type barcode defect detection. In stage 1, a YOLOv8n backbone localizes 1D and 2D barcodes in real time. In stage 2, a dual-branch network integrating ResNet50 and ViT-B/16 via hierarchical attention performs three-class classification on cropped regions of interest (ROIs): intact, defective, and non-barcode. Experiments conducted on the public BarBeR dataset, covering planar/non-planar surfaces, varying illumination, and sensor noise, show that Y8-LiBARNet achieves a detection-stage mAP@0.5 = 0.984 (1D: 0.992; 2D: 0.977) with a peak F1 score of 0.970. Subsequent defect classification attains 0.925 accuracy, 0.925 recall, and a 0.919 F1 score. Compared with single-branch baselines, our framework improves overall accuracy by 1.8–3.4% and enhances defective barcode recall by 8.9%. A Cohen’s kappa of 0.920 indicates strong label consistency and model robustness. These results demonstrate that Y8-LiBARNet delivers high-precision real-time performance, providing a practical solution for industrial barcode quality inspection. |
| format | Article |
| id | doaj-art-d27321580533419ea7c54e728bcddea8 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-d27321580533419ea7c54e728bcddea82025-08-20T04:00:54ZengMDPI AGApplied Sciences2076-34172025-07-011515817610.3390/app15158176Research on a Multi-Type Barcode Defect Detection Model Based on Machine VisionGanglong Duan0Shaoyang Zhang1Yanying Shang2Yongcheng Shao3Yuqi Han4Faculty of Economics and Management, Xi’an University of Technology, Xi’an 710048, ChinaFaculty of Economics and Management, Xi’an University of Technology, Xi’an 710048, ChinaFaculty of Economics and Management, Xi’an University of Technology, Xi’an 710048, ChinaFaculty of Economics and Management, Xi’an University of Technology, Xi’an 710048, ChinaFaculty of Economics and Management, Xi’an University of Technology, Xi’an 710048, ChinaBarcodes are ubiquitous in manufacturing and logistics, but defects can reduce decoding efficiency and disrupt the supply chain. Existing studies primarily focus on a single barcode type or rely on small-scale datasets, limiting generalizability. We propose Y8-LiBAR Net, a lightweight two-stage framework for multi-type barcode defect detection. In stage 1, a YOLOv8n backbone localizes 1D and 2D barcodes in real time. In stage 2, a dual-branch network integrating ResNet50 and ViT-B/16 via hierarchical attention performs three-class classification on cropped regions of interest (ROIs): intact, defective, and non-barcode. Experiments conducted on the public BarBeR dataset, covering planar/non-planar surfaces, varying illumination, and sensor noise, show that Y8-LiBARNet achieves a detection-stage mAP@0.5 = 0.984 (1D: 0.992; 2D: 0.977) with a peak F1 score of 0.970. Subsequent defect classification attains 0.925 accuracy, 0.925 recall, and a 0.919 F1 score. Compared with single-branch baselines, our framework improves overall accuracy by 1.8–3.4% and enhances defective barcode recall by 8.9%. A Cohen’s kappa of 0.920 indicates strong label consistency and model robustness. These results demonstrate that Y8-LiBARNet delivers high-precision real-time performance, providing a practical solution for industrial barcode quality inspection.https://www.mdpi.com/2076-3417/15/15/8176computer visiondefect detectionmulti-type barcodesY8-LiBAR Net |
| spellingShingle | Ganglong Duan Shaoyang Zhang Yanying Shang Yongcheng Shao Yuqi Han Research on a Multi-Type Barcode Defect Detection Model Based on Machine Vision Applied Sciences computer vision defect detection multi-type barcodes Y8-LiBAR Net |
| title | Research on a Multi-Type Barcode Defect Detection Model Based on Machine Vision |
| title_full | Research on a Multi-Type Barcode Defect Detection Model Based on Machine Vision |
| title_fullStr | Research on a Multi-Type Barcode Defect Detection Model Based on Machine Vision |
| title_full_unstemmed | Research on a Multi-Type Barcode Defect Detection Model Based on Machine Vision |
| title_short | Research on a Multi-Type Barcode Defect Detection Model Based on Machine Vision |
| title_sort | research on a multi type barcode defect detection model based on machine vision |
| topic | computer vision defect detection multi-type barcodes Y8-LiBAR Net |
| url | https://www.mdpi.com/2076-3417/15/15/8176 |
| work_keys_str_mv | AT ganglongduan researchonamultitypebarcodedefectdetectionmodelbasedonmachinevision AT shaoyangzhang researchonamultitypebarcodedefectdetectionmodelbasedonmachinevision AT yanyingshang researchonamultitypebarcodedefectdetectionmodelbasedonmachinevision AT yongchengshao researchonamultitypebarcodedefectdetectionmodelbasedonmachinevision AT yuqihan researchonamultitypebarcodedefectdetectionmodelbasedonmachinevision |