Thai Traffic Sign Detection and Recognition for Driver Assistance

2018 ◽  
Author(s):  
Sakan Promlainak ◽  
Kuntpong Woraratpanya ◽  
Jirapat Kuengwong ◽  
Yoshimitsu Kuroki
Keyword(s):  
Driver Assistance ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

scholarly journals Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2093 ◽  
Author(s):  
Safat B. Wali ◽  
Majid A. Abdullah ◽  
Mahammad A. Hannan ◽  
Aini Hussain ◽  
Salina A. Samad ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Current Trends ◽  
Sign Detection ◽  
Comprehensive Survey ◽  
Future Vision ◽  
Traffic Sign Detection ◽  
Detection And Recognition

The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

scholarly journals Traffic Sign Detection Based on Convolutional Neural Network

2021 ◽  
Vol 9 (8) ◽  
pp. 2926-2934
Author(s):  
Arjun Dileep
Keyword(s):  
Recognition Rate ◽  
Vital Role ◽  
Motive Force ◽  
Image Process ◽  
Traffic Sign ◽  
The Road ◽  
Sign Detection ◽  
Help Systems ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Abstract: In today's world, nearly everything we have a tendency to do has been simplified by machine-driven tasks. In a trial to specialize in the road whereas driving, drivers usually miss out on signs on the facet of the road, that can be dangerous for them and for the folks around them. This drawback may be avoided if there was AN economical thanks to inform the motive force while not having them to shift their focus. Traffic Sign Detection and Recognition (TSDR) plays a vital role here by detection and recognizing a symptom, therefore notifying the motive force of any coming signs. This not solely ensures road safety, however additionally permits the motive force to be at very little a lot of ease whereas driving on tough or new roads. Another normally long-faced drawback isn't having the ability to know the which means of the sign. With the assistance of this Advanced Driver help Systems (ADAS) application, drivers can not face the matter of understanding what the sign says. during this paper, we have a tendency to propose a way for Traffic Sign Detection and Recognition exploitation image process for the detection of a symptom and a Convolutional Neural Networks (CNN) for the popularity of the sign. CNNs have a high recognition rate, therefore creating it fascinating to use for implementing varied laptop vision tasks. TensorFlow is employed for the implementation of the CNN. Keywords: actitvity recognition; knowledge collection; knowledge preprocessing; coaching CNN model ;evaluating model; predicting the result.

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