Citation

Khan JA, Yeo D, Shin H. Sensors (Basel) 2018; 18(11): s18113776.

Affiliation

Division of Electrical Engineering, Hanyang University ERICA, Ansan 426-791, Korea. shin@hanyang.ac.kr.

Copyright

(Copyright © 2018, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s18113776

PMID

30400629

Abstract

In this paper, we propose a new Intelligent Traffic Sign Recognition (ITSR) system with illumination preprocessing capability. Our proposed Dark Area Sensitive Tone Mapping (DASTM) technique can enhance the illumination of only dark regions of an image with little impact on bright regions. We used this technique as a pre-processing module for our new traffic sign recognition system. We combined DASTM with a TS detector, an optimized version of YOLOv3 for the detection of three classes of traffic signs. We trained ITSR on a dataset of Korean traffic signs with prohibitory, mandatory, and danger classes. We achieved Mean Average Precision (MAP) value of 90.07% (previous best result was 86.61%) on challenging Korean Traffic Sign Detection (KTSD) dataset and 100% on German Traffic Sign Detection Benchmark (GTSDB). Result comparisons of ITSR with latest D-Patches, TS detector, and YOLOv3 show that our new ITSR significantly outperforms in recognition performance.

Language: en

Keywords

Dark Area Sensitive Tone Mapping (DASTM); Korean Traffic Sign Detection; classical tone mapping; luminance enhancement