论文标题
使用FCN检测交通车道
Traffic Lane Detection using FCN
论文作者
论文摘要
自动泳道检测是一种至关重要的技术，它使自动驾驶汽车能够在多车道的城市驾驶环境中正确定位自己。但是，在各种天气条件下检测各种路标是传统图像处理或计算机视觉技术的一项具有挑战性的任务。近年来，事实证明，深度学习和神经网络的应用非常有效。在这个项目中，我们为车道检测设计了一个编码器解码器，完全卷积的网络。该模型应用于现实世界中的大型数据集，并达到了优于我们的基线模型的准确性。
Automatic lane detection is a crucial technology that enables self-driving cars to properly position themselves in a multi-lane urban driving environments. However, detecting diverse road markings in various weather conditions is a challenging task for conventional image processing or computer vision techniques. In recent years, the application of Deep Learning and Neural Networks in this area has proven to be very effective. In this project, we designed an Encoder- Decoder, Fully Convolutional Network for lane detection. This model was applied to a real-world large scale dataset and achieved a level of accuracy that outperformed our baseline model.
