自举构建极端场景：面向自动飞行安全关键型探测与规避的高分辨率图像修复
Bootstrapping Corner Cases: High-Resolution Inpainting for Safety Critical Detect and Avoid for Automated Flying
AI总结：
针对无人机探测与规避任务中极端场景数据集稀缺、检测率低的问题，研究提出用图像修复技术自举构建含极端场景的高分辨率数据集，验证了方法有效性并公开数据集。
摘要：
现代机器学习技术已展现出巨大潜力，尤其是在相机图像的目标检测方面。因此，它们也被用于实现安全关键型自动化流程，例如无人机自主飞行。我们针对“探测与规避（Detect and Avoid）”中的目标检测开展了一项研究，这是无人机的一项安全关键功能，用于在自动飞行期间探测空中交通以保障安全。一个棘手的问题是生成优质且规模足够大的数据集，因为探测场景本身就属于极端情况（corner case）。大多数模型都受限于原始数据中真值（ground truth）不足的问题，例如记录的空中交通场景或小型飞机的正面飞行场景，这往往会导致检测率不佳且存在安全隐患。我们通过使用图像修复（inpainting）来自举构建数据集来解决这一问题，使数据集明确包含原始数据中的极端场景。我们概述了各类图像修复方法和生成式模型，并针对一个小型标注数据集给出了一套示例处理流程。我们通过生成一个高分辨率数据集验证了我们的方法，该数据集已公开，并将其提供给一个完全基于真实数据训练的独立目标检测器进行测试。
英文摘要：
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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