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Sensors 2018, 18(11), 3776; https://doi.org/10.3390/s18113776

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

Division of Electrical Engineering, Hanyang University ERICA, Ansan 426-791, Korea
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Received: 3 October 2018 / Revised: 31 October 2018 / Accepted: 2 November 2018 / Published: 5 November 2018
(This article belongs to the Section Intelligent Sensors)
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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. View Full-Text
Keywords: Korean Traffic Sign Detection; Dark Area Sensitive Tone Mapping (DASTM); classical tone mapping; luminance enhancement Korean Traffic Sign Detection; Dark Area Sensitive Tone Mapping (DASTM); classical tone mapping; luminance enhancement
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Khan, J.A.; Yeo, D.; Shin, H. New Dark Area Sensitive Tone Mapping for Deep Learning Based Traffic Sign Recognition. Sensors 2018, 18, 3776.

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