17 2

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

Title
New Dark Area Sensitive Tone Mapping for Deep Learning Based Traffic Sign Recognition
Author
신현철
Keywords
Korean Traffic Sign Detection; Dark Area Sensitive Tone Mapping (DASTM); classical tone mapping; luminance enhancement
Issue Date
2018-11
Publisher
MDPI
Citation
SENSORS, v. 18, No. 11, Article no. 3776
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.
URI
https://www.mdpi.com/1424-8220/18/11/3776http://repository.hanyang.ac.kr/handle/20.500.11754/121519
ISSN
1424-8220
DOI
10.3390/s18113776
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
Files in This Item:
2018.11_신현철_New Dark Area Sensitive Tone Mapping for Deep Learning Based Traffic Sign Recognition.pdfDownload
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE