EAGER: Exploratory Research on Deriving Flight Information from Drone Imagery for Safety Compliance

在美国，娱乐性无人机的使用正在迅速增加。美国联邦航空管理局（FAA）制定了安全规定，如飞行太高、太快、在限制区域等，但没有办法发现大规模的违规行为。此外，无人机用户不知道或不关心这些规定，因为它们是自我执行的。无人机用户将包括违反安全规定的飞行图像在内的大量图像上传到互联网。这些图像通常是飞行的唯一证据，因此一个有趣的研究问题是图像分析是否可以单独用于从飞行图像中检测违规行为。这个项目的总体目标是一种自动化的方法来识别互联网上大量可用的无人机图像中的特定违规实例。这将提供有关违反条例程度的宝贵资料。它也可以用来追捕特定的违规者。该项目将与加州大学无人机系统（UAS）安全卓越中心（http://uassafety.ucmerced.edu/）合作完成。该中心为十个校区UC系统的UAS的法规遵从、风险管理和安全操作提供专业知识、支持和培训。检测飞行是否超过美国联邦航空局规定的400英尺限制将作为概念验证。一个两步的过程将首先估计图像的空间分辨率（即每像素米），然后使用相机规格的知识或估计来计算高度。如果成功，概念验证可以扩展到其他违规行为，如飞行太快，在人群上方，能见度差等。估算高架图像的空间分辨率和高度是一个新颖的问题，所提出的方法具有新颖性、挑战性和风险。该项目有望取得重大成果。目前还没有办法大规模地检测违规行为，因此这将是解决这一日益重要问题的第一个解决方案。而且，除了高度估计之外，更广泛的无人机图像分析问题将从了解空间分辨率中受益。结果、数据集和其他项目工件将通过项目网站提供。

Recreational drone use is increasing rapidly in the United States. The Federal Aviation Administration (FAA) has established safety regulations such as flying too high, too fast, in restricted areas, etc. but there is no way to detect violations on a large scale. Further, drone users are unaware of or unconcerned about the regulations since they are self-enforced. Drone users upload large amounts of imagery to the Internet including that from flights which violate the safety regulations. This imagery is often the only evidence of the flights and so an interesting research question is whether image analysis can be used to detect violations from the flight imagery alone. The overarching goal of this project is an automated method to identify specific instances of violations in the large amounts of drone imagery available on the Internet. This would provide valuable information regarding the extent to which the regulations are being violated. It could also be used to pursue specific violators. The project will be done in collaboration with the University of California Center of Excellence on Unmanned Aerial Systems (UAS) Safety (http://uassafety.ucmerced.edu/). This Center provides expertise, support, and training for regulatory compliance, risk management, and the safe operation of UAS across the ten campus UC system.Detecting whether a flight is above the 400 ft limit specified by the FAA will serve as a proof-of-concept. A two-step process will first estimate the spatial resolution (i.e., meters per pixel) of the imagery and then use knowledge or estimates of the camera specifications to compute the height. If successful, the proof-of-concept can be extended to other violations such as flying too fast, above crowds, in poor visibility, etc. Estimating the spatial resolution and height of overhead imagery are novel problems, and the proposed approach is novel, challenging and risky. The project stands to make significant gains. There is currently no way to detect violations on a large scale and so this would be the first solution to this increasingly important problem. And, a broad range of drone image analysis problems beyond height estimation would benefit from knowing the spatial resolution. Results, datasets, and other project artifacts will be made available through the project website.