关键词: 信号灯检测, 遗传算法, 深度神经网络, 候选区域选择

Abstract: Traffic light detection is one of the key techniques of advanced driver assistance system and an important research direction for the on-board environment perception in autonomous vehicles. In view of the problem that general object detection algorithms are not suitable for small objects like traffic lights and the lack of real-time sliding window detection algorithms, a novel traffic light detection method is proposed in this paper, covering the generation of the candidate regions of traffic lights based on genetic optimization and the locating and classification of traffic lights based on deep neural network, in which the former, as the focus of the study, includes three parts: the common feature region extraction of traffic lights, the parameter sampling of the candidate region of traffic lights based on importance sampling and the parameter optimization of the candidate region of traffic lights based on genetic algorithm. Compared with existing traffic light detection methods, the method proposed can effectively detect and classify the horizontal rows and vertical columns of traffic lights with red and green colors, and round and arrow shapes. The comparative experiments on a public traffic light dataset show that the proposed method has a high recall rate for traffic lights, and can effectively distinguish different types of traffic lights

Key words: traffic light detection, genetic algorithm, deep neural network, candidate region selection