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.

对环境的感知是实现无人驾驶的前提。正确检测车道线和驾驶车辆是无人驾驶的关键技术。本文主要针对传统的车道检测算法在夜雨等复杂环境下精度较低的问题，提出了一种基于动态关注区域选择和萤火虫算法的车道检测与识别方法。首先，对捕获的车道图像进行失真校正，灰度和模糊图像预处理，然后根据消失点确定ROI的高度，通过对前一帧车道线的识别，动态调整ROI的宽度，实现动态ROI调整，消除干扰因素，最大程度地减少计算量。最后，针对传统方法中Canny算子对噪声敏感的问题，提出了一种改进的萤火虫算法进行边缘检测。斜率限制的渐进概率霍夫变换用于检测被分成几个框的ROI的直线，最小二乘法用于拟合多个检测到的直线以提取车道线。实验结果表明，本文提出的方法能够在复杂环境下很好地实现车道线检测，平均准确率达96.37％，每帧平均检测时间仅为118 ms。最后，针对传统方法中Canny算子对噪声敏感的问题，提出了一种改进的萤火虫算法进行边缘检测。斜率限制的渐进概率霍夫变换用于检测被分成几个框的ROI的直线，最小二乘法用于拟合多个检测到的直线以提取车道线。实验结果表明，本文提出的方法能够在复杂环境下很好地实现车道线检测，平均准确率达96.37％，每帧平均检测时间仅为118 ms。最后，针对传统方法中Canny算子对噪声敏感的问题，提出了一种改进的萤火虫算法进行边缘检测。斜率限制的渐进概率霍夫变换用于检测被分成几个框的ROI的直线，最小二乘法用于拟合多个检测到的直线以提取车道线。实验结果表明，本文提出的方法在复杂环境下能够很好地实现车道线检测，平均准确率达到96.37％，每帧平均检测时间仅为118 ms。斜率限制的渐进概率霍夫变换用于检测被分成几个框的ROI的直线，最小二乘法用于拟合多个检测到的直线以提取车道线。实验结果表明，本文提出的方法能够在复杂环境下很好地实现车道线检测，平均准确率达96.37％，每帧平均检测时间仅为118 ms。斜率限制的渐进概率霍夫变换用于检测被分成几个框的ROI的直线，最小二乘法用于拟合多个检测到的直线以提取车道线。实验结果表明，本文提出的方法能够在复杂环境下很好地实现车道线检测，平均准确率达96.37％，每帧平均检测时间仅为118 ms。