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

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 na...

Full description

Saved in:
Bibliographic Details
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
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 .
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2024.3479884