Vision-based Traffic Sign Compliance Evaluation using Convolutional Neural Network

被引：0

作者：

Roxas, Edison A. ^{[1
]}

Acilo, Joshua N. ^{[1
]}

Vicerra, Ryan Rhay P. ^{[1
]}

Dadios, Elmer P. ^{[1
]}

Bandala, Argel A. ^{[1
]}

机构：

[1] De la Salle Univ, Gokongwei Coll Engn, 2401 Taft Ave, Manila 1004, Philippines

来源：

PROCEEDINGS OF 4TH IEEE INTERNATIONAL CONFERENCE ON APPLIED SYSTEM INNOVATION 2018 ( IEEE ICASI 2018 ) | 2018年

关键词：

Computer vision; Traffic sign monitoring and evaluation; Traffic sign compliance and standard; AlexNet;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Manual monitoring of road signs compliance procedures are adapted by developing countries. As effective as this method is, the amount of time and funds needed to cover a large area is quite alarming. Thus, a need for a vision - based traffic sign detection and recognition system. However, while a majority of researches using machine vision focuses on the development of a robust real -time traffic sign recognition system, researches addressing the issue of the sign compliance and standardization is lacking.

引用

页码：120 / 123

页数：4

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