天津工业大学学报2024，Vol.43Issue(4)：50-59,74,11.DOI:10.3969/j.issn.1671-024x.2024.04.009
基于链接关系预测的弯曲密集型商品文本检测
Text detection of curved and dense products based on link relationship prediction
摘要
Abstract
A detection framework consisting of two sub-networks,text detection network based on relational prediction(RPTNet)is proposed to solve the problem of error detection caused by curved and dense texts in the text detec-tion task of commodity packaging images.In the text component detection network,local and global features are extracted using a parallel downsampling structure of convolutional neural network and self-attention.A dilated feature enhancement module(DFM)is added to the downsampling structure to reduce the information loss of the deep feature maps.The feature pyramid network is combined with the multi-level attention fusion module(MAFM)in upsampling structure to enhance the connections between different features and the text detector de-tects the text components from the upsampled feature maps.In the link relational prediction network,a relational reasoning framework based on graph convolutional network is used to predict the deep similarity between the text component and its neighbors,and a bi-directional long short-term memory network is used to aggregate the text components into text instances.In order to verify the detection performance of RPTNet,a text detection dataset CPTD1500 composed of commodity packaging images is constructed.The test results show that the effectiveness of the proposed RPTNet is verified by two publicly available text datasets,CTW-1500 and Total-Text.And the recall and F value of RPTNet on CPTD1500 are 85.4％and 87.5％,respectively,which are superior to current mainstream algorithms.关键词
文本检测/卷积神经网络/自注意力/特征融合/图卷积网络/双向长短时记忆网络Key words
text detection/convolutional neural network/self-attention/feature fusion/graph convolutional network/bi-di-rectional long short-term memory network分类
信息技术与安全科学引用本文复制引用
耿磊,李嘉琛,刘彦北,李月龙,李晓捷..基于链接关系预测的弯曲密集型商品文本检测[J].天津工业大学学报,2024,43(4):50-59,74,11.基金项目
国家自然科学基金资助项目(61771340) （61771340）
天津市科技计划资助项目(20YDTPJC00110) （20YDTPJC00110）
