Airport runway segmentation can effectively reduce the accident rate during the landing phase, which has the largest risk of flight accidents. With the rapid development of deep learning (DL), related methods achieve good performance on segmentation tasks and can be well adapted to complex scenes. However, the lack of large-scale, publicly available datasets in this field makes the development of methods based on DL difficult. Therefore, we propose a benchmark for airport runway segmentation, named BARS. Additionally, a semiautomatic annotation pipeline is designed to reduce the annotation workload. BARS has the largest dataset with the richest categories and the only instance annotation in the field. The dataset, which was collected using the X-Plane simulation platform, contains 10,256 images and 30,201 instances with three categories. We evaluate eleven representative instance segmentation methods on BARS and analyze their performance. Based on the characteristic of an airport runway with a regular shape, we propose a plug-and-play smoothing postprocessing module (SPM) and a contour point constraint loss (CPCL) function to smooth segmentation results for mask-based and contour-based methods, respectively. Furthermore, a novel evaluation metric named average smoothness (AS) is developed to measure smoothness. The experiments show that existing instance segmentation methods can achieve prediction results with good performance on BARS. SPM and CPCL can effectively enhance the AS metric while modestly improving accuracy. Our work will be available at https://github.com/c-wenhui/BARS.

翻译：机场跑道分割能有效降低着陆阶段的事故率，而着陆阶段是飞行事故风险最大的环节。随着深度学习（DL）的快速发展，相关方法在分割任务中取得了良好的性能，并能很好地适应复杂场景。然而，该领域缺乏大规模公开数据集，使得基于DL的方法开发困难重重。为此，我们构建了一个机场跑道分割基准数据集BARS，并设计了一种半自动标注流程以减轻标注工作量。BARS拥有该领域规模最大、类别最丰富的数据集，且是唯一提供实例标注的数据集。该数据集通过X-Plane仿真平台采集，包含10,256张图像和30,201个实例，涵盖三个类别。我们基于BARS评估了十一种代表性实例分割方法的性能，并分析了其表现。针对机场跑道形状规则的特点，我们分别提出了即插即用的平滑后处理模块（SPM）和轮廓点约束损失函数（CPCL），以优化基于掩膜和基于轮廓方法的分割结果平滑度。此外，我们设计了新颖的评估指标——平均平滑度（AS）来量化分割平滑性。实验表明，现有实例分割方法在BARS上能取得优异预测结果，而SPM和CPCL在有效提升AS指标的同时，还能适度提高分割精度。我们的工作将发布于https://github.com/c-wenhui/BARS。