TTDNet: An End-to-End Traffic Text Detection Framework for Open Driving Environments

• Published in: IEEE transactions on intelligent transportation systems 2024-12, Vol.25 (12), p.19770-19784
• Main Authors: Wang, Runmin, Zhu, Yanbin, Chen, Hua, Zhu, Zhenlin, Zhang, Xiangyu, Ding, Yajun, Qian, Shengyou, Gao, Changxin, Liu, Li, Sang, Nong
• Format: Article
• Language: English
• Subjects: Accuracy; convolutional neural network; feature enhancement; Feature extraction; Image enhancement; Image segmentation; Intelligent transportation; Intelligent transportation systems; Meteorology; Prediction algorithms; Shape; Text detection; Text recognition; traffic text
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2024.3479884

Traffic text detection is crucial for traffic scene understanding in intelligent transportation systems (ITS). Although natural scene text detection has been extensively studied, yielding noteworthy results, little research has focused on traffic text detection. Traffic text, as a special type of natural scene text, faces not only the general challenges of natural scene text detection but also the significant impact of false alarms from non-traffic texts on system performance. In light of these challenges, we propose an end-to-end traffic text detection framework that can effectively detect traffic text captured by in-vehicle cameras in various driving scenarios. The key contributions of our proposed approach are: (1) an Image Enhancement Module designed to remove fog and enhance low-quality images; (2) a plug-and-play Text Feature Enhancement Module; (3) a Joint Loss Function; and (4) the creation of an open driving environment traffic text dataset (named ODETT-3000) containing various traffic environments. Comprehensive experimental studies have verified that our method achieves state-of-the-art performance on traffic text datasets such as CTST-1600, TPD, and ODETT-3000, as well as promising results on public natural scene text datasets such as MSRA-TD500, ICDAR 2015, and CTW 1500, thereby showcasing the superior performance and adaptability of our method. The code and our dataset (ODETT-3000) will be available at https://github.com/yanbin-zhu/TTDNet .