Many famous researchers at home and

Many famous researchers at home and abroad have put forward the recognition algorithm of traffic signal lights. The existing traffic signal lamp detection methods mainly include color feature-based detection methods and shape feature-based detection methods. In 2006, Maphipa R. Yelal et al. realized the detection of traffic signal lights based on the method of color segmentation [1]. Firstly, the algorithm converts RGB images into Lab images, performs color segmentation in the Lab color space, clustering the pixels and obtaining the candidate area of the signal lamp. Then, the edge information of the candidate area is analyzed, and finally the identification and detection of the traffic signal lamp is completed. This method reduces the parameters required for color segmentation and simplifies the debugging process by converting the color space, but the disadvantage is that the performance of the program cannot be completely guaranteed in the complex environment. Kuo-Hao Lu et al. also used color space to extract candidate areas of traffic signal lights [2]. Compared with the previous method, they labeled the candidate areas and carried out template feature matching of circle and arrow shapes in each candidate area according to self-defined shape feature rules. This method can classify the circular TRAFFIC SIGNAL LIGHT DETECTION AND RECOGNITION BASED ON CANNY OPERATOR. LIU YUMING, GUO SHUQING168 JOURNAL OF MEASUREMENTS IN ENGINEERING. SEPTEMBER 2021, VOLUME 9, ISSUE 3 and arrowhead shapes of traffic signal lights when the image of traffic signal lamp is large (the area of the signal lamp is > 165 pixels). However, the recognition distance should not be too far when collecting by on-board camera, which is not conducive to the path planning and decision-making of intelligent vehicles at intersections. In 2009, Masako Omachi proposed traffic signal light detection based on color and edge [3]. The color was firstly normalized and then segmented to obtain candidate regions of the color features of traffic signal lights. Sobel algorithm is used to extract the edge of the candidate region, and then Hough transform is used to identify the circular region and complete the detection of traffic signal lights. The disadvantage of this algorithm is that the anti-interference is not strong and the interference of other external factors such as car lights cannot be eliminated. Raoul de Charette et al. analyzed that most of the traffic signal light identification methods studied by predecessors are based on suspended traffic signal lights [4]. However, in the field of view of the on-board camera, the background of suspended signal lights is the sky with relatively simple background environment, which is not very suitable for the urban road environment with complex background. Therefore, a circular traffic light recognition algorithm based on brightness and shape is proposed. The algorithm first converts RGB images into grayscale images, then carries out top hat transformation on the grayscale images obtained, and then carries out binarization processing to obtain binarization images. Then, it filters the connected areas through morphological filtering, and finally uses the adaptive template to match ATM for recognition. The algorithm has strong robustness, but the method cannot recognize the traffic lights in the weather with too much illumination and in the environment with too much illumination.