Real-time vision-based Malaysian road sign recognition using an artificial neural network / Kh Tohidul Islam - Kh Tohidul , Islam (2017)
Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘Pedestrian Crossing’ indications. The system can help the driver to maintain a legal speed, obey local traffic instructions, or urban restrictions. A road sign recognition system can technically be developed as part of an intelligent transportation system that can continuously monitor the driver, the vehicle, and the road in order, for example, to inform the driver in time about upcoming decision points regarding navigation and potentially risky traffic situations. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real-world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. This hybrid color segmentation algorithm contains a RGB histogram equalization, RGB color segmentation, modified grayscale segmentation, binary image segmentation, and a shape matching algorithm. All these algorithms are tested using thousands of images. The hybrid color segmentation algorithm has eventually been chosen for this proposed system as it shows the best performance for detection of road signs. In the second stage, an introduced robust Custom Feature Extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of F-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. The accuracy of the developed system is comparatively high and the processing time is comparatively low that can be useful for classifying road signs particularly on highways around Malaysia. This low FPR can increase the system stability and dependability in real-time applications.