论文标题
用混合锚驱动的序列分类进行超快速深入泳道检测
Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification
论文作者
论文摘要
现代方法主要将车道检测视为像素细分的问题，该问题正在努力解决效率问题和诸如严重闭塞和极端照明条件之类的挑战性情况。受到人类感知的启发，在严重遮挡和极端照明条件下对车道的认识主要基于上下文和全球信息。在这一观察过程中，我们提出了一种针对超快速速度的新颖，简单但有效的配方，以及具有挑战性的场景问题。具体而言，我们将车道检测过程视为使用全局特征的锚定序列分类问题。首先，我们在一系列混合（行和列）锚点上代表具有稀疏坐标的车道。借助锚驱动的代表，我们然后将车道检测任务重新制定为序数分类问题，以获取车道的坐标。我们的方法可以通过锚驱动的表示可以大大降低计算成本。使用顺序分类公式的大型接受场特性，我们还可以处理具有挑战性的情况。在四个车道检测数据集上进行的广泛实验表明，我们的方法可以在速度和准确性方面实现最先进的性能。轻量级版本甚至可以每秒达到300帧（FPS）。我们的代码在https://github.com/cfzd/ultra-fast-lane-detection-v2上。
Modern methods mainly regard lane detection as a problem of pixel-wise segmentation, which is struggling to address the problems of efficiency and challenging scenarios like severe occlusions and extreme lighting conditions. Inspired by human perception, the recognition of lanes under severe occlusions and extreme lighting conditions is mainly based on contextual and global information. Motivated by this observation, we propose a novel, simple, yet effective formulation aiming at ultra fast speed and the problem of challenging scenarios. Specifically, we treat the process of lane detection as an anchor-driven ordinal classification problem using global features. First, we represent lanes with sparse coordinates on a series of hybrid (row and column) anchors. With the help of the anchor-driven representation, we then reformulate the lane detection task as an ordinal classification problem to get the coordinates of lanes. Our method could significantly reduce the computational cost with the anchor-driven representation. Using the large receptive field property of the ordinal classification formulation, we could also handle challenging scenarios. Extensive experiments on four lane detection datasets show that our method could achieve state-of-the-art performance in terms of both speed and accuracy. A lightweight version could even achieve 300+ frames per second(FPS). Our code is at https://github.com/cfzd/Ultra-Fast-Lane-Detection-v2.
