【摘要】 随着人们生活水平的提高,对于车辆行驶的安全问题也日益关注度。目前主要通过高级辅助驾驶系统(ADAS)减少交通事故,其关键是为车辆驾驶决策提供必要环境的道路识别技术。主流车道检测算法大多不能兼有实时性与准确性,为解决该问题,提出基于兴趣区域的窗口流车道检测与跟踪技术,并利用深度学习使得检测精度不断提高。在实验室模拟情况中,通过对5000张道路图片的检测,该算法共计准确识别图片4993张,模糊识别图片5张,错误识别图片2张,识别率达到99.86%。在实际道路识别中,可满足实时视频的检测,具有很好的实时性、准确性、鲁棒性和抗干扰性,对无人驾驶有一定的辅助作用。更多还原

【Abstract】 Under the current social environment,people pay more and more attention to the safety of vehicle driving.At present,the most important way to reduce traffic accidents is to assist driving through advanced assisted driving system(ADAS),in which the key part of road identification technology can provide necessary road environment information for the driving decision-making part of the vehicle.Most of the current mainstream lane detection algorithms cannot be real-time and accurate at the same time.Therefore,a new window-by-window lane detection and tracking technology based on region of interest is proposed,in which deep learning is used to improve the accuracy of the algorithm.In the laboratory simulation,through the detection of 5000 road images,the algorithm accurately identified 4993 images,5 fuzzy identification images,2 wrong identification images,the recognition rate reached 99.86%.In the actual road identification,it can meet the real-time video detection,with good real-time,accuracy,robustness and anti-interference,and has a certain auxiliary role for unmanned driving.更多还原