QR code detection and recognition in industrial production environment based on SSD and image processing

Jianqiao Xiong; Linli Zhou

doi:10.1117/12.3011655

7 December 2023 QR code detection and recognition in industrial production environment based on SSD and image processing

Jianqiao Xiong, Linli Zhou

Author Affiliations +

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129412L (2023) https://doi.org/10.1117/12.3011655
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

QR code is widely used in production management. For example, we scan the QR code printed on the fertilizer bag during the production of fertilizer to record production information. We propose a method to intelligently obtain the information carried by QR code through machine vision, combining deep learning and image processing technology. We obtain the original image through the industrial camera, locate the QR code area in the image based on the SSD target detection model, use the image processing method to rectify the QR code area, and finally decode it through the Zxing library. Through the real production environment test, the recognition rate of the method is 98.6%, and the average detection and recognition time of a frame image is 94 ms.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianqiao Xiong and Linli Zhou "QR code detection and recognition in industrial production environment based on SSD and image processing", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129412L (7 December 2023); https://doi.org/10.1117/12.3011655

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KEYWORDS

Image processing

Object detection

Image compression

Cameras

RGB color model

Environmental sensing

Detection and tracking algorithms

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