Tlb‑yolo: a rapid and efcient real‑time algorithm for box-type classification and barcode recognition on the moving conveying and sorting systems

Tlb‑yolo: a rapid and efcient real‑time algorithm for box-type classification and barcode recognition on the moving conveying and sorting systems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Tlb‑yolo: a rapid and efcient real‑time algorithm for box-type classification and barcode recognition on the moving conveying and sorting systems Liang Shen, Xin Li, Wei Yang, Qiang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4981502/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract In recent years, camera-based vision sensors utilizing YOLO neural network algorithms have increasingly replaced traditional sensors such as photoelectric sensors. Due to their advantages in cost and efficiency, applications in object classification and barcode detection have become more widespread in industrial and logistics sectors. This study addresses the real-time items classification and barcode detection by proposing the Transmission Line Barcode YOLO (TLB-YOLO) model. We made improvements to the YOLOv8 model by introducing several new components. We integrated the Coordinate Attention (CA) mechanism into the backbone network to improve the model's sensitivity to object locations. The Wise-IoU loss function was employed to enhance localization accuracy, while the GSConv (Grouped Shuffle Convolution) and Slim Neck architecture were incorporated to boost detection accuracy and speed. The proposed model, with 3.8 million parameters and 8.5 GFLOPs, was trained on the COCO dataset, achieving an mAP0.5 of 68.1% and an mAP0.95 of 44.2%, which represent improvements of 7.9% and 5.2% over YOLOv8n, respectively. It attained a frame rate of 153.8 FPS. When retrained on a custom dataset incorporating synthetic data from Omniverse, the model demonstrated over 90% accuracy in detecting cardboard boxes, plastic containers, and barcodes. To resolve model export issues caused by the dynamic pooling layer, an equivalent code substitution was applied, enhancing inference speed. Testing on a Jetson Nano development board, with each experiment phase repeated 50 times, showed that the TLB-YOLO model achieved 100% detection accuracy for plastic containers side scanning, cardboard box top scanning, and barcode verification. The model’s detection accuracy and inference speed fully satisfy real-time detection requirements in practical scenarios. process optimization object detection barcode recognition improved YOLOv8 model simulation modeling Full Text Additional Declarations No competing interests reported. Supplementary Files Appendix1ImprovedModuleCode.docx Appendix2TensorRTRealTimeInferenceCode.docx Appendix3Barcodeinferencerecognitioncode.docx Appendix4HikvisionIndustrialCameraControlandImageProcessingcode.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Sep, 2024 Reviewers invited by journal 01 Sep, 2024 Editor assigned by journal 28 Aug, 2024 Submission checks completed at journal 28 Aug, 2024 First submitted to journal 27 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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