Acp-Net: Asymmetric Center Positioning Network for Real-Time Text Detection

作     者：Zhu, Boyuan Liu, Fagui Chen, Xi Tang, Quan Chen, C.L. Philip 

作者机构：South China University of Technology No.342 Outer Ring East Road Panyu Guangzhou510006 China Peng Cheng Laboratory No. 2 Xingke 1st Street Nanshan Shenzhen518055 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences No.95 Zhongguancun East Road Haidian Beijing100190 China 

出 版 物：《SSRN》 

年 卷 期：2024年

核心收录：

主　　题：Feature extraction 

摘      要：Scene text detection plays a crucial role in numerous application fields. However, despite the varying focus on real-time performance, almost all existing detection models employ the Feature Pyramid Network (FPN) structure for feature extraction, overlooking its inherent limitations. The integration of high-resolution multi-channel features within FPN demands significant computational resources. Although FPN which treats local and global features as equivalent, has been proven to be stable in various fields, it remains to be reconsidered whether this stability strategy is the most suitable for text features. To this end, we propose an Asymmetric Center Positioning Network (ACP-Net), which replaces the FPN structure and achieves precise and real-time text detection in complex scenarios. Specifically, we explore an asymmetric feature structure that includes two independent branches to acquire both global and local information, as well as an adaptive weighted fusion module to effectively capture the long-range dependencies from these two branches. Furthermore, we present a text center positioning module to enhance the understanding of text features by learning the feature centers. Comprehensive assessments conducted across various terminals have demonstrated the superiority of ACP-Net in terms of both accuracy and speed. © 2024, The Authors. All rights reserved.