1. Accurate detection of individual animals is integral to the management of vulnerable wildlife species, but often difficult and costly to achieve for species that occur over wide or inaccessible areas or engage in cryptic behaviours. There is a growing acceptance of the use of drones (also known as unmanned aerial vehicles, UAVs and remotely piloted aircraft systems, RPAS) to detect wildlife, largely because of the capacity for drones to rapidly cover large areas compared to ground survey methods. While drones can aid the capture of large amounts of imagery, detection requires either manual evaluation of the imagery or automated detection using machine learning algorithms. While manual evaluation of drone-acquired imagery is possible and sometimes necessary, the powerful combination of drones with automated detection of wildlife in this imagery is much faster and, in some cases, more accurate than using human observers. Despite the great potential of this emerging approach, most attention to date has been paid to the development of algorithms, and little is known about the constraints around successful detection (P. W. J. Baxter, and G. Hamilton, 2018, Ecosphere, 9, e02194).
2. We reviewed studies that were conducted over the last 5 years in which wildlife species were detected automatically in drone-acquired imagery to understand how technological constraints, environmental conditions and ecological traits of target species impact detection with automated methods.
3. From this review, we found that automated detection could be achieved for a wider range of species and under a greater variety of environmental conditions than reported in previous reviews of automated and manual detection in drone-acquired imagery. A high probability of automated detection could be achieved efficiently using fixed-wing platforms and RGB sensors for species that were large and occurred in open and homogeneous environments with little vegetation or variation in topography while infrared sensors and multirotor platforms were necessary to successfully detect small, elusive species in complex habitats.
4. The insight gained in this review could allow conservation managers to use drones and machine learning algorithms more accurately and efficiently to conduct abundance data on vulnerable populations that is critical to their conservation.

中文翻译：

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使用无人机自动检测野生动物：综合、机会和限制

1. 准确检测个体动物是脆弱野生动物物种管理不可或缺的一部分，但对于发生在广阔或无法进入的区域或从事神秘行为的物种而言，实现这一目标通常很困难且成本高昂。人们越来越多地接受使用无人机（也称为无人驾驶飞行器、无人机和遥控飞机系统，RPAS）来探测野生动物，这主要是因为与地面调查方法相比，无人机能够快速覆盖大面积区域。虽然无人机可以帮助捕获大量图像，但检测需要手动评估图像或使用机器学习算法进行自动检测。虽然手动评估无人机获取的图像是可能的，有时也是必要的，无人机与此图像中野生动物自动检测的强大组合比使用人类观察者更快，在某些情况下更准确。尽管这种新兴方法具有巨大潜力，但迄今为止，大多数注意力都集中在算法的开发上，并且对成功检测的限制知之甚少（PWJ Baxter 和 G. Hamilton，2018 年，生态圈，9，e02194）。
2. 我们回顾了过去 5 年中在无人机获取的图像中自动检测野生动物物种的研究，以了解目标物种的技术限制、环境条件和生态特征如何影响自动方法的检测。
3. 从这次审查中，我们发现可以在更广泛的物种和更多样化的环境条件下实现自动检测，而不是之前对无人机获取图像中的自动和手动检测的审查报告。使用固定翼平台和 RGB 传感器可以有效地实现高概率的自动检测，用于大型物种并且发生在植被或地形变化很少的开放和均质环境中，而红外传感器和多旋翼平台对于成功检测小型、复杂栖息地中难以捉摸的物种。
4. 本次审查中获得的见解可以让保护管理人员更准确、更有效地使用无人机和机器学习算法来处理对其保护至关重要的脆弱人群的丰度数据。