HTSTL: Head-and-Tail Search Network With Scale-Transfer Layer for Traffic Sign Text Detection

IEEE Access

Li Liu¹  Chengyu Guo¹  Runmin Wang²  Xiumei Li²  Xuan He²  Xing Chen²  Lv Wen²  Changxin Gao³ 

[1] Department of Information System and Management, National University of Defense Technology, Changsha, China;Hunan Provincial Key Laboratory of Intelligent Computing and Language Processing, Hunan Normal University, Changsha, China;School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China;

关键词: Scene text detection;    multi-oriented text;    convolutional neural network;    residual network;   

DOI  :  10.1109/ACCESS.2019.2936540

来源: DOAJ

【 摘 要 】

Although promising results have been achieved in the area of traffic sign detection, little attention has been paid to text detection on traffic signs. In fact, in today's popular driver-less automobile industry, traffic sign text which brings abundant and valuable traffic information plays an important and indispensable role. In this work, we design an effective detector for traffic sign text, whose pipeline only consists of a preprocessing module to tackle with some complex situations, a Fully Convolutional Network (FCN) in which a Scale-transfer layer is proposed to speed up the network and a simple post-processing step. Extensive experiments on the Chinese traffic sign text dataset (CTST-1600), ICDAR 2013 and MSRA-TD500 show that the proposed method has achieved the state-of-the-art results, which proves the ability of our detector on both particularity and universality applications. We collect the Chinese text-based traffic sign dataset named CTST-1600, and it can be found at https://github.com/pummi823/test/blob/master/CTST-1600.

【 授权许可】

Unknown