用图像修复技术增强极端罕见场景数据，提升无人机避障检测能力
Bootstrapping Corner Cases: High-Resolution Inpainting for Safety Critical Detect and Avoid for Automated Flying
- 通过图像修复自动生成稀缺的极端飞行场景数据
- 生成高分辨率数据集后，检测器在真实数据上表现显著提升
- 适合需要提升边缘场景检测鲁棒性的自动驾驶视觉系统
现代机器学习在摄像头图像目标检测方面展现出巨大潜力，被用于实现如无人机自主飞行等安全关键任务。本文研究无人机避障（Detect and Avoid）中的目标检测问题，该任务在极端情况下（如小飞机正面飞行、空中交通稀少）面临数据匮乏难题，导致检测性能差且存在安全隐患。针对此问题，我们采用图像修复方法对原始数据进行增强，主动生成包含边缘场景的高质量数据。我们综述了多种图像修复与生成模型，并展示了一个基于小规模标注数据的完整数据生成流程。通过构建高分辨率合成数据集并公开发布，验证了该方法的有效性：一个仅在真实数据上训练的独立检测器，在使用增强数据后检测率明显提高。
原文摘要 · Abstract (English)
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.
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