Bootstrapping Corner Cases: High-Resolution Inpainting for Safety Critical Detect and Avoid for Automated Flying

TL;DR

This paper introduces a high-resolution inpainting approach to generate large, diverse datasets of corner cases for improving object detection in safety-critical drone applications, enhancing detection robustness.

Contribution

It presents a novel inpainting pipeline to bootstrap datasets with corner cases, addressing data scarcity in safety-critical drone detection systems.

Findings

Generated high-resolution datasets improve detection of corner cases.

The inpainting method enhances dataset diversity for training.

Object detectors trained on generated data perform better in safety scenarios.

Abstract

Modern machine learning techniques have shown tremendous potential, especially for object detection on camera images. For this reason, they are also used to enable safety-critical automated processes such as autonomous drone flights. We present a study on object detection for Detect and Avoid, a safety critical function for drones that detects air traffic during automated flights for safety reasons. An ill-posed problem is the generation of good and especially large data sets, since detection itself is the corner case. Most models suffer from limited ground truth in raw data, \eg recorded air traffic or frontal flight with a small aircraft. It often leads to poor and critical detection rates. We overcome this problem by using inpainting methods to bootstrap the dataset such that it explicitly contains the corner cases of the raw data. We provide an overview of inpainting methods and generative models and present an example pipeline given a small annotated dataset. We validate our method by generating a high-resolution dataset, which we make publicly available and present it to an independent object detector that was fully trained on real data.