Traffic light recognition in varying illumination using deep learning and saliency map

Abstract

The accurate detection and recognition of traffic lights is important for autonomous vehicle navigation and advanced driver aid systems. In this paper, the authors present a traffic light recognition algorithm for varying illumination conditions using computer vision and machine learning. More specifically, a convolutional neural network is used to extract and detect features from visual camera images. To improve the recognition accuracy, an on-board global position system (GPS) sensor is employed to identify the region-of-interest, in the visual image, that contains the traffic light. In addition, a saliency map containing the traffic light location is generated using the normal illumination recognition to assist the recognition under low illumination conditions. The proposed algorithm was evaluated on the authors data sets acquired in a variety of real world environments and compared with the performance of a baseline traffic signal recognition algorithm. The experimental results demonstrate the high recognition accuracy of the proposed algorithm in varied illumination conditions.