This paper describes gaps in acquisition of drone imagery that impair the use with computer vision/machine learning (CV/ML) models and makes five recommendations to maximize image suitability for CV/ML post-processing. It describes a notional work process for the use of drones in wilderness search and rescue incidents. The large volume of data from the wide area search phase offers the greatest opportunity for CV/ML techniques because of the large number of images that would otherwise have to be manually inspected. The 2023 Wu-Murad search in Japan, one of the largest missing person searches conducted in that area, serves as a case study. Although drone teams conducting wide area searches may not know in advance if the data they collect is going to be used for CV/ML post-processing, there are data collection procedures that can improve the search in general with automated collection software. If the drone teams do expect to use CV/ML, then they can exploit knowledge about the model to further optimize flights.

翻译：本文阐述了无人机影像采集中存在的不足，这些不足会削弱其与计算机视觉/机器学习（CV/ML）模型的兼容性，并提出了五项建议以提升影像对CV/ML后处理的适用性。文章描述了无人机在野外搜救事件中的理想工作流程。广域搜索阶段产生的大量数据为CV/ML技术提供了最大潜力，因为通常这些海量图像需要人工逐一核查。以2023年日本吴-村田搜救行动（该区域规模最大的失踪人员搜索案例之一）为研究案例，尽管执行广域搜索的无人机团队可能事先不确定所采集数据是否用于CV/ML后处理，但采用自动化采集软件的数据收集流程可普遍提升搜索效率。若无人机团队预期使用CV/ML技术，则可利用对模型特性的认知进一步优化飞行方案。