This study presents an approach to lane detection involving the prediction of binary segmentation masks and per-pixel affinity fields. These affinity fields, along with the binary masks, can then be used to cluster lane pixels horizontally and vertically into corresponding lane instances in a post-processing step. This clustering is achieved through a simple row-by-row decoding process with little overhead; such an approach allows LaneAF to detect a variable number of lanes without assuming a fixed or maximum number of lanes. Moreover, this form of clustering is more interpretable in comparison to previous visual clustering approaches, and can be analyzed to identify and correct sources of error. Qualitative and quantitative results obtained on popular lane detection datasets demonstrate the model's ability to detect and cluster lanes effectively and robustly. Our proposed approach performs on par with state-of-the-art approaches on the limited TuSimple benchmark, and sets a new state-of-the-art on the challenging CULane dataset.
翻译:这项研究提出了一种对车道探测方法,包括预测二元分解面罩和双像体亲近场。这些近似场加上二元面罩可以用来将车道像素横向和垂直地分组成一个后处理步骤的相应车道情况。这种集群是通过简单的逐行解码过程实现的,而几乎没有间接费用;这种方法使LaneAF能够在不假定固定或最大车道数的情况下探测多条车道。此外,与以往的视觉集束方法相比,这种集群形式更容易解释,可以分析出错源。流行车道探测数据集取得的定性和定量结果显示了该模型能够有效和稳健地探测和分组车道。我们提议的办法与关于有限托斯伯基准的先进方法相当,并在具有挑战性的CURane数据集上设置新的最新技术。