BARS: A Benchmark for Airport Runway Segmentation

TL;DR

This paper introduces BARS, a large-scale airport runway segmentation benchmark with a new dataset, annotation pipeline, and methods to improve segmentation smoothness, facilitating advancements in runway safety and deep learning applications.

Contribution

The paper presents the first large-scale, publicly available dataset for airport runway segmentation, along with novel postprocessing and loss functions to enhance segmentation smoothness.

Findings

Existing segmentation methods perform well on BARS.

SPM and CPCL improve segmentation smoothness.

The dataset enables comprehensive evaluation of segmentation techniques.

Abstract

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.