Cigarette Laser Code Recognition Method Based on DBNet and CRNN Fusion Model

doi:10.3969/j.issn.1006-2475.2025.04.015

Abstract

Abstract: Cigarette laser code recognition is an important means of tobacco inspection work. Aiming at the problem of low detection and recognition rate due to the complex background of cigarette code， this paper proposes a cigarette code recognition method based on DBNet and CRNN. First， the DBNet model is used to detect the smoke code region， and the accurate positioning and extraction of the cigarette code is realized by introducing differentiable binarization. Then， the CRNN model is used to identify the features of the cigarette code region image after positioning and cropping processing， and the spatial and temporal features are obtained by combining the VGG network and the deep Bi-LSTM network to realize the accurate identification of the cigarette code. Experimental result has an accuracy rate of 91.9% in detection， 83.4% in recognition， the testing accuracy after deploying the mobile APP is 83.0%， which shows that the method proposed in this paper can provide accurate cigarette laser code recognition effect.

Key words: , cigarette laser code, target detection, text recognition, DBNet, CRNN

CLC Number:

TP391

MA Qi1, WEN Yudong1, LIANG Shangrong2, WANG Ke2. Cigarette Laser Code Recognition Method Based on DBNet and CRNN Fusion Model[J]. Computer and Modernization, 2025, 0(04): 96-102.

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