计算机与数字工程2025，Vol.53Issue(1)：196-201,6.DOI:10.3969/j.issn.1672-9722.2025.01.036
改进的DenseNet的密集场景文本检测方法
Improved DenseNet Text Detection Method for Dense Scenes
吕鹏鹏 1於跃成 1齐秀芳1
作者信息
- 1. 江苏科技大学计算机学院 镇江 212100
- 折叠
摘要
Abstract
Scene text detection is a challenging task in recent years.Aiming at the limited and missing features of dense text detection in natural scenes,a scene image oriented detection method is proposed.Firstly,the deformable ROI pool is used to re-place the average pooling layer,and the improved denseNet network is used as the feature extraction network to realize the adaptive local localization of different scale texts.Then,the multi-level image features are weighted by convolution attention module to en-hance the text features.In addition,the variable convolution is introduced to replace the ordinary convolution in feature fusion,so that the direction vector of the convolution kernel is added to adjust,and the sampling grid is free to deform,so that the shape of the convolution kernel is closer to the shape of the text.Finally,an auxiliary bidirectional gating loop unit is introduced in the output lay-er to gather the text area.Compared with the existing methods,the model improves nearly 1.11%on ICDAR2013 data set and nearly 1.17%on ICDAR2015 data set,and improves the detection accuracy to a certain extent.关键词
图像处理/文本检测/可变卷积/自然场景Key words
image processing/text detection/deformable convolution/natural scene分类
信息技术与安全科学引用本文复制引用
吕鹏鹏,於跃成,齐秀芳..改进的DenseNet的密集场景文本检测方法[J].计算机与数字工程,2025,53(1):196-201,6.
