New Dark Area Sensitive Tone Mapping for Deep Learning Based Traffic Sign Recognition

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 11; p. 3776
• Main Authors: Khan, Jameel Ahmed, Yeo, Donghoon, Shin, Hyunchul
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 05.11.2018; MDPI AG
• Subjects: classical tone mapping; Dark Area Sensitive Tone Mapping (DASTM); Korean Traffic Sign Detection; luminance enhancement; luminance enhancement; Dark Area Sensitive Tone Mapping (DASTM); classical tone mapping; Korean Traffic Sign Detection
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18113776

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.