雾天环境下前车车距测量方法研究

Study on the distance measurement of approaching vehicles in fog

为解决雾天环境下道路上车辆与前车车距测量问题,构造车载雾天图像快速处理以及前车车距测量实验平台。以暗通道算法为基础,基于能见度图像分割算法估算大气光值,利用双边滤波细化折射率图,在分割区域上进行不同程度去雾,有效解决暗通道算法应用在道路图像上产生的色彩失真、对比度过低等问题。利用边缘检测算法、霍夫变换算法完成对车辆边框的检测,搭建测距模型测量出前方车辆的距离。结果表明,构造的平台能够在能见度小于100 m的浓雾环境下测量出前方车辆车距,并能及时告警。

To address the challenges related to distance measurement of an approaching vehicle in fog,we developed an experimental platform to rapid image processing and real-time distance measurement.Firstly,we down-sampled the images through the dark channel algorithm to estimate atmospheric light values.Then,we introduced a tolerance mechanism to deal with the bright regions that do not satisfy the dark channel prior.This tolerance mechanism corrected the estimate with incorrect refractive index of such regions and effectively mitigated the issues of color distortion and low contrast.Secondly,we detected the vertical edges of an approaching vehicle using the edge detection and the improved Hough transform algorithms.Finally,we measured the safe distance from the approaching vehicle using the model.The results shows that the platform developed in this study can effectively measure the distance of the approaching vehiclein fog with a visibility<100 m,and can alert drivers in a timely and effective manner.