Deleting a branch is permanent. It CANNOT be undone. Continue?
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》
H-Net比用固定的转换矩阵要好,用3阶多项式拟合要比2阶好,用了透视变换比不用好。
车道线识别深度学习面临的挑战:
(1)车道线这种细长的形态结构,需要更加强大的高低层次特征融合,来同时获取全局的空间结构关系,和细节处的定位精度。
(2)车道线的形态有很多不确定性,比如被遮挡,磨损,以及道路变化时本身的不连续性。需要网络针对这些情况有较强的推测能力。
(3)车辆的偏离或换道过程会产生自车所在车道的切换,车道线也会发生左/右线的切换。一些提前给车道线赋值固定序号的方法,在换道过程中会产生歧义的情况。
车道线检测方法分为两种:一种是传统算法,主要基于边缘特征或者是图像分割,通过图像预处理,特征提取等方式与卡尔曼滤波器等算法结合,在识别出车道线后采用后处理的方式形成最终的车道。然而这种传统方法易受到光照变化,行驶车辆,道路破损等干扰,导致效果欠佳。近年来,深度学习方法利用网络模型自动学习目标特征,具有较强的泛化能力,可以有效提高目标检测的准确率。得益于卷积神经网络的强大特征提取能力,性能也在不断提升。
yhytr