An Efficient Algorithm for Traffic Sign Detection
Aryuanto Soetedjo*, and Koichi Yamada**
*Information Science and Control Engineering, Nagaoka University of Technology, 1603-1 Kamitomiokamachi, Nagaoka, Niigata 940-2188, Japan
**Management and Information Systems Science, Nagaoka University of Technology, 1603-1 Kamitomiokamachi, Nagaoka, Niigata 940-2188, Japan
We propose an efficient algorithm for detecting traffic signs in images. Geometric fragmentation detects circular red traffic signs in an image by finding and combining the left and right fragments of elliptical objects to increase the accuracy of detection and cope with occlusion. The search for fragments resembles a genetic algorithm (GA) in that it uses the terms individual, population, crossover, and objective function used in the GA. It is different in that it conducts a concurrent random search in a small two-dimensional space devised heuristically. The objective function for evaluating individuals is devised to increase detection accuracy and reduce computation time. The algorithm was tested for detecting circular red traffic signs both from artificial sign images and real scene images. Experimental results demonstrated that the proposed algorithm has higher detection, fewer false alarms, and lower computation cost than GA-based ellipse detection. Compared to conventional template matching, the proposed algorithm performs better in detection and execution time and does not require a large number of carefully prepared templates.
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