机械与电子2026，Vol.44Issue(7)：61-70,10.
融合DBNet与LCNet-SVTR的卷烟鉴别检验返样单自动识别方法
Automatic Recognition Method for Tobacco Identification and Inspection Return Samples Integrating DBNet and LCNet-SVTR
摘要
Abstract
Manual processing of tobacco identification and inspection return samples in the tobacco in-dustry is time-consuming,labor-intensive,and error-prone,failing to meet the demand for efficient and accurate data processing.To overcome these limitations,this paper proposes a deep learning-based auto-matic recognition method that integrates the differentiable binarization network(DBNet),a lightweight convolu-tional network LCNet,and the sequence vision Transformer(SVTR).The method first preprocesses return sample images through grayscale normalization,contrast enhancement,scale standardization,and data augmentation to sup-press the adverse effects of scanning noise,uneven illumination,and seal interference on recognition performance.Subsequently,in the text detection stage,the DBNet model,which adopts the idea of differentiable binarization,is employed to jointly learn the text probability maps and an adaptive threshold map,thereby achieving the segmenta-tion and localization of dense short text regions.In the text recognition stage,an LCNet-SVTR recognition model is constructed by combining LCNet and SVTR,which captures fine-grained local character detail while modeling global contextual semantics.Finally,the text recognition results of the return samples are output through post-processing decoding.Experimental results demonstrate that the proposed method achieves a recognition accuracy exceeding 89%and a normalized edit distance above 98%,offering an effective solution for automated document processing in the tobacco industry.关键词
深度学习/文本检测/轻量级卷积网络/卷烟鉴别检验识别/序列视觉Key words
deep learning/text detection/lightweight convolutional network/tobacco identification and inspection recognition/sequence vision分类
信息技术与安全科学引用本文复制引用
张海婷,林杨,金曦,李秀梅,李书琼,袁庆霓..融合DBNet与LCNet-SVTR的卷烟鉴别检验返样单自动识别方法[J].机械与电子,2026,44(7):61-70,10.基金项目
贵州省烟草公司贵阳市公司科技项目(黔烟筑科[2024]4号) （黔烟筑科[2024]4号）
