A Study on Recognizing Industrial Barcodes in Low-Light High-Speed Conditions Using Deep Learning and Image Enhancement Techniques

A method integrating deep learning and image enhancement is proposed for industrial barcode recognition in low-light and complex conditions. The SCI and Night-Enhance models enhance image clarity, while YOLO v8 provides precise barcode localization and segmentation. Experimental results demonstrate a 95% recognition accuracy and 0.200-second processing time, outperforming traditional methods in both efficiency and adaptability for industrial applications.

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Publication Details

Published
2024-12-20
DOI
https://doi.org/10.1109/iucc65928.2024.00108
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
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article

A Study on Recognizing Industrial Barcodes in Low-Light High-Speed Conditions Using Deep Learning and Image Enhancement Techniques

Zhixian Deng, Junhao Cheng
Industrial Vision Systems and Defect Detection
article

A Study on Recognizing Industrial Barcodes in Low-Light High-Speed Conditions Using Deep Learning and Image Enhancement Techniques

Zhixian Deng, Junhao Cheng
article en

Abstract

A method integrating deep learning and image enhancement is proposed for industrial barcode recognition in low-light and complex conditions. The SCI and Night-Enhance models enhance image clarity, while YOLO v8 provides precise barcode localization and segmentation. Experimental results demonstrate a 95% recognition accuracy and 0.200-second processing time, outperforming traditional methods in both efficiency and adaptability for industrial applications.

Institute of Software (CN), Software Engineering Institute of Guangzhou
Industry, innovation and infrastructure
Openalex Percentile: Top 56%
Industrial Vision Systems and Defect Detection
DOISourceOpenAlex
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A Study on Recognizing Industrial Barcodes in Low-Light High-Speed Conditions Using Deep Learning and Image Enhancement Techniques — Zhixian Deng, Junhao Cheng · (2024) | TGRS Research Map | TGRS