Traffic signal light detection and recognition based on canny operator

Abstract

In this paper, five classical edge detection operators are compared, and then a traffic signal light detection and recognition scheme that can be used for intelligent connected vehicles is implemented. Firstly, the image to be processed is obtained by detecting the traffic signal light through the vision sensor. The image is preprocessed: the color space of the image is converted from RGB space to HSV space. Through the grayscale, histogram equalization, image binarization processing, using the morphological closure operation, the five operators are compared in noise sensitivity, positioning accuracy and signal-to-noise ratio, the Canny edge detection operator is selected for image edge detection, and the target recognition area is obtained. Finally, using the histogram drawn, the number of red, green and yellow pixel points in the histogram can be clearly counted, and the color with the largest number of pixel points can be identified as the color of the identified traffic signal light, and the identification of the traffic signal light can be completed. The actual pictures are simulated on the MATLAB, which verifies the feasibility of the proposed method of traffic signal light recognition method based on Canny operator in this paper, which can correctly identify the color of the traffic signal light.