Context: Monitoring is an essential part of managing invasive species; however, accurate, cost-effective detection techniques are necessary for it to be routinely undertaken. Current detection techniques for invasive deer are time consuming, expensive and have associated biases, which may be overcome by exploiting new technologies.

Aims: We assessed the accuracy and cost effectiveness of automated detection methods in comparison to manual detection of thermal footage of deer captured by remotely piloted aircraft systems.

Methods: Thermal footage captured by RPAS was assessed using an algorithm combining two object-detection techniques, namely, YOLO and Faster-RCNN. The number of deer found using manual review on each sampling day was compared with the number of deer found on each day using machine learning. Detection rates were compared across survey areas and sampling occasions.

Key results: Overall, there was no difference in the mean number of deer detected using manual and that detected by automated review (P = 0.057). The automated-detection algorithm identified between 66.7% and 100% of deer detected using manual review of thermal imagery on all but one of the sampling days. There was no difference in the mean proportion of deer detected using either manual or automated review at three repeated sampling events (P = 0.174). However, identifying deer using the automated review algorithm was 84% cheaper than the cost of manual review. Low cloud cover appeared to affect detectability using the automated review algorithm.

Conclusions: Automated methods provide a fast and effective way to detect deer. For maximum effectiveness, imagery that encompasses a range of environments should be used as part of the training dataset, as well as large groups for herding species. Adequate sensing conditions are essential to gain accurate counts of deer by automated detection.

Implications: Machine learning in combination with RPAS may decrease the cost and improve the detection and monitoring of invasive species.

背景：监测是管理入侵物种的重要组成部分；然而，准确、经济有效的检测技术对于定期进行是必要的。目前针对入侵鹿的检测技术耗时、昂贵且存在相关偏差，可以通过开发新技术来克服这些偏差。

目的：我们评估了自动检测方法的准确性和成本效益，与手动检测遥控飞机系统捕获的鹿的热影像进行比较。

方法：使用结合两种物体检测技术（YOLO 和 Faster-RCNN）的算法对 RPAS 捕获的热影像进行评估。在每个采样日使用人工审查发现的鹿数量与使用机器学习在每天发现的鹿数量进行比较。对不同调查区域和抽样场合的检出率进行了比较。

主要结果：总体而言，使用手动检测的鹿的平均数量与通过自动审查检测到的鹿的平均数量没有差异（P  = 0.057）。自动检测算法识别出 66.7% 到 100% 之间的鹿，在除采样日之外的所有日子里都使用热成像手动审查检测到。在三个重复抽样事件中，使用手动或自动审查检测到的鹿的平均比例没有差异（P  = 0.174）。然而，使用自动审查算法识别鹿比人工审查的成本便宜 84%。使用自动审查算法，低云量似乎会影响可检测性。

结论：自动化方法提供了一种快速有效的检测鹿的方法。为获得最大效果，应将包含一系列环境的图像以及用于放牧物种的大组用作训练数据集的一部分。适当的传感条件对于通过自动检测获得准确的鹿数量至关重要。

启示：机器学习与 RPAS 相结合可能会降低成本并改善入侵物种的检测和监测。