面向嵌入式平台的车道线检测方法

Lane line detection method for embedded platform

车道线检测在自动驾驶和高级辅助驾驶中起着举足轻重的作用,然而,传统的车道线检测技术鲁棒性较差,而大多数基于深度学习的方法复杂度又较高,难以在嵌入式平台实时应用。提出一种面向嵌入式平台的轻量级车道线检测网络,将车道线检测转化为语义分割问题,该网络借鉴U-Net与Segnet网络结构,使用了小尺度卷积等轻量化组件设计计算高效的语义分割网络。在检测车道线的基础上,计算车辆距离两侧车道线的距离,以及车道线的曲率,同时当车辆偏离车道线或检测出现异常时进行预警,最后将整个系统移植到海思平台。实验结果表明:该系统具有较高的检测精度以及检测速度,准确率达到97.5%,速度达到50 FPS,满足实时性要求,因此该系统能够用于面向嵌入式平台的实时车道线的检测、测距、曲率计算以及预警。

Lane line detection plays a pivotal role in autonomous driving and advanced assisted driving.However,traditional lane line detection technology was less robust,and most methods based on deep learning were more complex and difficult to embed platform real-time application.A lightweight lane line detection network for embedded platforms was proposed,which converts lane line detection into a semantic segmentation problem.The network draws on U-Net and Segnet network structures,and uses small-scale convolution and other lightweight components to design and calculate efficiently semantic segmentation network.Based on the detection of the lane line,calculate the distance between the vehicle and the lane line on both sides,as well as the curvature of the lane line,and give an early warning when the vehicle deviates from the lane line or the detection was abnormal.Finally,the entire system was transplanted to the HiSilicon platform.Experimental results show that the system has high detection accuracy and detection speed,the accuracy rate reaches 97.5%,the speed reaches 50 FPS,and meets real-time requirements.Therefore,the system can be used for real-time lane line detection,ranging,and distance measurement for embedded platforms.Curvature calculation and early warning.