摘要
为了解决复杂光照下的缩微道路车道线检测问题,提出了一种适用于复杂光照下的缩微道路车道线检测方法。首先运用朴素贝叶斯对不同光照下的车道图像进行分类,然后对分类后的车道图像采取相应的图像处理方法,最后运用改进的大津法和改进概率霍夫变换来检测车道线。对多段不同光照模式下的车道图像进行的仿真实验表明,该方法对缩微道路车道线检测的成功率达到95.5%,具有很强的鲁棒性和抗干扰能力。
In order to resolve the problem of lane detection in Micro-traffic under complex illumination,a lane detection method was proposed for the Micro-traffic under complex illumination.Firstly,the lane images for different illumination are classified by using Naive Bayes.Then,the classified lane images are proceed by using the corresponding image processing method.Finally,improved Otsu and revised Probabilistic Hough Transform are introduced to detect lanes.Simulation experiment on the different lane images under different illumination shows that the lane detection success rate in Micro-traffic is up to 95.5％ and the method possesses strong robustness and anti-interference.
出处
《计算机科学》
CSCD
北大核心
2014年第3期46-49,70,共5页
Computer Science
基金
国家自然科学基金(61073146
61272060)资助
