Lane Line Detection and Recognition Based on Dynamic ROI and Modified Firefly Algorithm

Perception of the environment is the prerequisite for the realization of unmanned driving. Correctly detecting the lane line and navigating the vehicle is the key technology in unmanned driving. This paper mainly aims at the low accuracy of the traditional lane detection algorithm in the complex environment such as night and rain, and proposes a lane detection and recognition method based on dynamic region of interest (ROI) selection and firefly algorithm. First, perform distortion correction on the captured lane image, gray scale and blur image preprocessing, and then determine the height of the ROI based on the vanishing point, and dynamically adjust the width of the ROI based on the recognition of the lane line in the previous frame to achieve dynamic ROI adjust to eliminate interference factors and reduce the amount of calculation to the greatest extent. Finally, to solve the problem that the canny operator is sensitive to noise in the traditional method, an improved firefly algorithm is proposed for edge detection. The slope-limited progressive probability Hough transform is used to detect the straight line of the ROI divided into several boxes, and the least square method is used to fit several detected straight lines to extract lane lines. Experimental results show that the method we proposed can achieve lane line detection well in complex environments, with an average accuracy rate of 96.37%, and an average detection time per frame of only 118 ms.