Korean Traffic Sign Detection Using Deep Learning

被引：0

作者：

Manocha, Prateek ^{[1
]}

Kumar, Ayush ^{[1
]}

Khan, Jameel Ahmed ^{[2
]}

Shin, Hyunchul ^{[2
]}

机构：

[1] Indian Inst Technol, Dept Biotechnol, Dept Elect & Commun Engn, Gauhati, India

[2] Hanyang Univ, Div Elect Engn, Ansan, South Korea

来源：

2018 INTERNATIONAL SOC DESIGN CONFERENCE (ISOCC) | 2018年

关键词：

Korean Traffic Signs; D-Patches; YOLOv3;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：247 / 248

页数：2

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