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

EAGER：从无人机图像中获取飞行信息以确保安全合规的探索性研究

• 批准号: 1747535
• 负责人: Shawn Newsam
• 金额: $ 19.94万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1747535&HistoricalAwards=false
• 关键词: EAGER; Exploratory; Research; Deriving; Flight

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.

侦察无人机的使用在美国迅速增加。联邦航空管理局（FAA）制定了飞行过高、过快、在限制区域等安全规定，但没有办法大规模地发现违规行为。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。 无人机用户将大量图像上传到互联网，包括违反安全规定的航班。这一图像往往是飞行的唯一证据，因此一个有趣的研究问题是，图像分析是否可以用于仅从飞行图像中发现违规行为。该项目的总体目标是一种自动化方法，用于识别互联网上大量无人机图像中的具体违规行为。这将提供关于违反条例程度的宝贵信息。它还可以用来追捕特定的违法者。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。该中心提供专业知识，支持和培训，以确保法规遵从性，风险管理和UAS在十个校园UC系统中的安全运行。检测飞行是否超过FAA规定的400英尺限制将作为概念验证。两步过程将首先估计空间分辨率（即，每像素米），然后使用相机规格的知识或估计来计算高度。如果成功的话，概念验证可以扩展到其他违规行为，如飞行太快，人群以上，能见度差等估计的空间分辨率和高度的开销图像是新的问题，所提出的方法是新颖的，具有挑战性和风险。该项目有望取得重大成果。目前没有办法大规模地发现违规行为，因此这将是解决这一日益重要的问题的第一个办法。而且，除了高度估计之外的各种无人机图像分析问题都将受益于空间分辨率。 结果、数据集和其他项目工件将通过项目网站提供。

项目成果

Estimating the Spatial Resolution of Very High-Resolution Overhead Imagery

• DOI: 10.1145/3356471.3365241
• 发表时间: 2019-11
• 期刊: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery
• 作者: Haolin Liang;S. Newsam

Estimating The Spatial Resolution of Overhead Imagery Using Convolutional Neural Networks

• DOI: 10.1109/icip.2019.8802954
• 发表时间: 2019-09
• 期刊: 2019 IEEE International Conference on Image Processing (ICIP)
• 作者: Haolin Liang;S. Newsam