Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
Korean Traffic Sign Detection Using Deep Learning
- Authors
- Manocha, P.; Kumar, A.; Khan, J.A.; Shin, H.
- Issue Date
- 2019
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- D-Patches; Korean Traffic Signs; YOLOv3
- Citation
- Proceedings - International SoC Design Conference 2018, ISOCC 2018, pp.247 - 248
- Indexed
- SCOPUS
- Journal Title
- Proceedings - International SoC Design Conference 2018, ISOCC 2018
- Start Page
- 247
- End Page
- 248
- ISSN
- 0000-0000
- Abstract
- In this paper, we present a new optimized architecture modified from YOLOv3 to detect three different classes of challenging Korean Traffic Sign Detection (KTSD) dataset. We optimized the new neural network called TS detector with denser grid size, and optimized anchor box size to detect prohibitory, mandatory, and danger classes of KTSD dataset. We trained this architecture on our Korean traffic sign dataset to achieve the mAP value of 86.61%. Our results are significantly better than original YOLOv3 and D-Patches algorithm in terms of mAP value and CPU time. © 2018 IEEE.
- Files in This Item
- There are no files associated with this item.
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.