CLRerNet：利用LaneIoU提升车道检测的置信度
CLRerNet: Improving Confidence of Lane Detection with LaneIoU
AI总结：
提出CLRerNet，利用LaneIoU改进车道检测置信度，通过考虑局部角度优化目标分配与损失，在CULane和CurveLanes上显著超越现有方法。
摘要：
车道标记检测是自动驾驶和驾驶员辅助系统的关键组成部分。现代基于行表示（row-based representation）的深度车道检测方法在车道检测基准上表现出优异的性能。通过初步的oracle实验，我们首先解耦了车道表示组件，以确定我们方法的方向。我们表明，正确的车道位置已经存在于现有基于行的检测器的预测中，而准确表示与真实值（ground truths）的交并比（IoU）的置信度分数是最有益的。基于这一发现，我们提出了LaneIoU，它通过考虑局部车道角度，更好地与指标相关联。我们开发了一种新颖的检测器，命名为CLRerNet，其特点是将LaneIoU用于目标分配成本和损失函数，旨在提高置信度分数的质量。通过包括交叉验证在内的仔细且公平的基准测试，我们证明CLRerNet大幅优于最先进的方法——在CULane上享受81.43%的F1分数，而现有方法为80.47%，在CurveLanes上为86.47%，而现有方法为86.10%。
英文摘要：
Lane marker detection is a crucial component of the autonomous driving and driver assistance systems. Modern deep lane detection methods with row-based lane representation exhibit excellent performance on lane detection benchmarks. Through preliminary oracle experiments, we firstly disentangle the lane representation components to determine the direction of our approach. We show that correct lane positions are already among the predictions of an existing row-based detector, and the confidence scores that accurately represent intersection-over-union (IoU) with ground truths are the most beneficial. Based on the finding, we propose LaneIoU that better correlates with the metric, by taking the local lane angles into consideration. We develop a novel detector coined CLRerNet featuring LaneIoU for the target assignment cost and loss functions aiming at the improved quality of confidence scores. Through careful and fair benchmark including cross validation, we demonstrate that CLRerNet outperforms the state-of-the-art by a large margin - enjoying F1 score of 81.43% compared with 80.47% of the existing method on CULane, and 86.47% compared with 86.10% on CurveLanes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
