Robust Traffic Light Detection and Classification Under Day and Night Conditions

Abstract

Recently, traffic light detection and classification systems have been studied and developed to build an autonomous car by many research institutes, universities, and companies. However, the results of existing traffic light detection systems are still not stable under day and night conditions. It is difficult to detect the location of traffic light due to their small size. Moreover, traffic lights’ shapes are also similar to advertisement lights in a city road. Therefore, this paper proposed a new approach to improve the performance of existing traffic light detection systems by using the benefits of hand-crafted features and deep learning techniques. Experimental results show that the proposed system obtained the detection rate of 80% under night conditions, while the color-based density method only got the detection rate of 50.43% under night conditions.