Synthetic Data for Robust Runway Detection

TL;DR

This paper explores using synthetic images generated from a flight simulator to improve runway detection in autonomous landing systems, demonstrating enhanced robustness and accuracy with domain adaptation techniques.

Contribution

It introduces a synthetic data generation method for runway detection and shows its effectiveness combined with real data and domain adaptation.

Findings

Synthetic data improves detection accuracy.

Domain adaptation enhances robustness to adverse conditions.

Synthetic images help cover rare scenarios.

Abstract

Deep vision models are now mature enough to be integrated in industrial and possibly critical applications such as autonomous navigation. Yet, data collection and labeling to train such models requires too much efforts and costs for a single company or product. This drawback is more significant in critical applications, where training data must include all possible conditions including rare scenarios. In this perspective, generating synthetic images is an appealing solution, since it allows a cheap yet reliable covering of all the conditions and environments, if the impact of the synthetic-to-real distribution shift is mitigated. In this article, we consider the case of runway detection that is a critical part in autonomous landing systems developed by aircraft manufacturers. We propose an image generation approach based on a commercial flight simulator that complements a few annotated real images. By controlling the image generation and the integration of real and synthetic data, we show that standard object detection models can achieve accurate prediction. We also evaluate their robustness with respect to adverse conditions, in our case nighttime images, that were not represented in the real data, and show the interest of using a customized domain adaptation strategy.