为满足智能车载视频取证设备对车辆违章行为取证时检测车道线准确性和时效性的要求,提出一种基于感兴趣区域模型的结构化道路车道线的快速检测算法.该算法首先基于结构化道路具有车道线与路面对比度高的特点,采用Soberx边缘检测算子检测车道的边缘信息,然后在感兴趣区域模型基础上,采用改进的Hough变换检测出可能的车道线.为准确提取车道线完成车道的准确检测,该算法基于透视图像消失点原理,采用逆透视变换消除伪车道线完成车道准确定位.实验表明,该算法定位精度高、快速快、鲁棒性好,能够满足高速公路智能车载视频设备取证的性能要求.%In order to meet the requirements of the accuracy and timeliness of lane detection in intelligent vehicle video forensics equipment for vehicle violation behavior forensics, a fast lane detection algorithm based on region of interest (ROI) model is proposed. Firstly, the edge detection operator is used to detect the lane edge information based on the characteristic that the lane and road have high contrast. Then the improved Hough transform is used to detect the possible lane based on the region of interest model. In order to extract the lane accurately, the algorithm is based on the vanishing point principle of the perspective image, and uses the inverse perspective transformation to eliminate the pseudo lane and complete the lane accurate positioning. Experiments show that the algorithm has the advantages of high positioning accuracy, high speed, and good robustness, which can meet the performance requirements of highway intelligent vehicle video equipment forensics.
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