Bootstrapping Corner Cases: High-Resolution Inpainting for Safety Critical Detect and Avoid for Automated Flying
Journal:
arXiv
Published Date:
Jan 14, 2025
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.
