Recent work done on lane detection has been able to detect lanes accurately in complex scenarios, yet many fail to deliver real-time performance specifically with limited computational resources. In this work, we propose SwiftLane: a simple and light-weight, end-to-end deep learning based framework, coupled with the row-wise classification formulation for fast and efficient lane detection. This framework is supplemented with a false positive suppression algorithm and a curve fitting technique to further increase the accuracy. Our method achieves an inference speed of 411 frames per second, surpassing state-of-the-art in terms of speed while achieving comparable results in terms of accuracy on the popular CULane benchmark dataset. In addition, our proposed framework together with TensorRT optimization facilitates real-time lane detection on a Nvidia Jetson AGX Xavier as an embedded system while achieving a high inference speed of 56 frames per second.
翻译:在这项工作中,我们提议SwiftLane:一个简单和轻量、端到端深深学习基础框架,加上快速和高效道探测的行式分类配方;这一框架得到一个假正压算法和曲线适配技术的补充,以进一步提高准确性;我们的方法达到每秒411个框架的推断速度,速度超过最新水平,同时在流行的CULane基准数据集的准确性方面取得可比结果;此外,我们提议的框架与TensorRT优化一起,便利在Nvidia Jetson AgX Xavier作为嵌入系统上实时测道,同时达到56个框架的高度推断速度。