Detection and Information Restoration of Highly Soiled QR Code

WANG Yongchao; CAO Peng

doi:10.19554/j.cnki.1001-3563.2026.05.014

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (5) : 119-129. DOI: 10.19554/j.cnki.1001-3563.2026.05.014

Automatic and Intelligent Technology

Detection and Information Restoration of Highly Soiled QR Code

WANG Yongchao, CAO Peng^*

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Abstract

The work aims to propose a highly soiled QR code image detection and information restoration technology to solve the problem that QR codes are severely damaged and difficult to read due to multiple factors, even human factors. Firstly, a cross-scale connection and attention mechanism was introduced to optimize the YOLO11n network, enabling rapid and accurate location of highly soiled QR codes in complex backgrounds. Secondly, a structure-constrained diffusion model was presented to restore the information of the positioned image. Different from the traditional methods, the model presented in this paper took the structural information such as the positioning symbol, correction symbol and encoding grid of the QR code as the prior constraints in the restoration process, making the restored image structurally correct and standardized, and effectively improving the probability of being readable and decoded. The experimental results showed that the detection accuracy (mAP@0.5-0.95) of the optimized YOLO11n model presented in this paper on the self-built highly soiled QR code dataset and the public MSQ dataset reached 75.3% and 77.1%, respectively. Besides, when the structure-constrained diffusion model dealt with QR codes with a soiled area exceeding 40%, its decoding success rate reached 54%. The model proposed in this paper can effectively solve the restoration problem of highly soiled QR codes and the problem of correct reading.

Key words

QR Code / YOLO11n / diffusion model / attention mechanism / image restoration

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WANG Yongchao, CAO Peng. Detection and Information Restoration of Highly Soiled QR Code[J]. Packaging Engineering. 2026, 47(5): 119-129 https://doi.org/10.19554/j.cnki.1001-3563.2026.05.014

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