Publication: Traffic sign recognition using neural network on open CV: toward intelligent vehicle/driver assistance system
Issued Date
2007
Resource Type
Language
eng
Rights
Mahidol University
Rights Holder(s)
Center for Biomedical and Robotics Technology (BART LAB), Department of Biomedical Engineering, Faculty of Engineering, Mahidol University
Suggested Citation
Auranuch Lorsakul (2007). Traffic sign recognition using neural network on open CV: toward intelligent vehicle/driver assistance system. Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/3369
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Traffic sign recognition using neural network on open CV: toward intelligent vehicle/driver assistance system
Author(s)
Corresponding Author(s)
Abstract
Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or
prohibit certain actions. Fast real-time and robust automatic traffic sign detection and recognition
can support and disburden the driver and significantly increase driving safety and comfort.
Automatic recognition of traffic signs is also important for an automated intelligent driving vehicle
or for driver assistance systems. This paper presents a study to recognize traffic sign patterns using
Neural Network technique. Images are pre-processed with several image processing techniques,
such as, threshold techniques, Gaussian filter, Canny edge detection, Contour and Fit Ellipse.
Then, the Neural Networks stages are performed to recognize the traffic sign patterns. The system
is trained and validated to find the best network architecture. The experimental results show highly
accurate classifications of traffic sign patterns with complex background images as well as the
results accomplish in reducing the computational cost of this proposed method.