  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.

Computer Vision and Pattern Recognition;Artificial Intelligence