Abstract
Traffic sign detection and recognition (TSDR) is one of the main area of research in autonomous vehicles and Advanced Driving Assistance System (ADAS). In this paper a method is proposed to detect and classify the prohibitory subset of German Traffic Sign data set. The traffic sign detection module utilizes adaptive color segmentation based on mean saturation value of local neighborhood, and Circular Hough Transform (CHT) to locate the traffic signs in the input images. Adaptive color thresholding shows improvement in segmenting traffic signs where images have uneven lighting or very high or low contrast levels, compared to global thresholding. Furthermore, number of false alarms are minimized by utilizing an additional validation stage. For the recognition phase of the algorithm, multiple deep Convolutional Neural Networks (CNN) with different structures are developed from scratch to compare their performance and identify the network with highest accuracy.
Published Version
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