An Evaluation of the Factors Affecting 'Poacher' Detection with Drones and the Efficacy of Machine-Learning for Detection.
An Evaluation of the Factors Affecting 'Poacher' Detection with Drones and the Efficacy of Machine-Learning for Detection.
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评估影响无人机检测“偷猎者”的因素以及机器学习检测的有效性。
DOI：
10.3390/s21124074
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发表时间：
2021-06-13
期刊：
影响因子：
--
通讯作者：
Wich SA
中科院分区：
文献类型：
--
作者：
Doull KE;Chalmers C;Fergus P;Longmore S;Piel AK;Wich SA
无人机越来越多地用于保护，以解决非法偷猎动物的问题。为此目的使用无人机的一个重要方面是建立技术和环境因素，以增加在检测偷猎者时成功的机会。最近的研究集中在调查这些因素，这项研究建立在此基础上，并探索机器学习自动检测的有效性。在自愿测试对象的实验环境中，测试了各种因素对检测概率的影响：相机类型（可见光谱，RGB和热红外，TIR），一天中的时间，相机角度，树冠密度，和行走/静止的测试对象。志愿者和自动检测软件都对无人机镜头进行了手动分析。使用具有logit链接函数的广义线性模型对两种分析类型的数据进行统计分析。研究结果得出结论，使用TIR相机提高了检测概率，特别是在黎明和90°相机角度下。倾斜角度在RGB飞行期间更有效，并且行走/静止的测试对象不影响两个相机的检测。随着植被覆盖的增加，检测概率降低。机器学习软件的成功检测概率为0.558，但它产生的误报几乎是手动分析的五倍。然而，人工分析产生的假阴性是自动检测的2.5倍。尽管在这项研究中，手动分析比自动检测产生更多的真阳性检测，但自动化软件提供了有希望的成功结果，自动化方法相对于手动分析的优势使其成为一种有前途的工具，有可能成功地纳入反偷猎策略。
Drones are being increasingly used in conservation to tackle the illegal poaching of animals. An important aspect of using drones for this purpose is establishing the technological and the environmental factors that increase the chances of success when detecting poachers. Recent studies focused on investigating these factors, and this research builds upon this as well as exploring the efficacy of machine-learning for automated detection. In an experimental setting with voluntary test subjects, various factors were tested for their effect on detection probability: camera type (visible spectrum, RGB, and thermal infrared, TIR), time of day, camera angle, canopy density, and walking/stationary test subjects. The drone footage was analysed both manually by volunteers and through automated detection software. A generalised linear model with a logit link function was used to statistically analyse the data for both types of analysis. The findings concluded that using a TIR camera improved detection probability, particularly at dawn and with a 90° camera angle. An oblique angle was more effective during RGB flights, and walking/stationary test subjects did not influence detection with both cameras. Probability of detection decreased with increasing vegetation cover. Machine-learning software had a successful detection probability of 0.558, however, it produced nearly five times more false positives than manual analysis. Manual analysis, however, produced 2.5 times more false negatives than automated detection. Despite manual analysis producing more true positive detections than automated detection in this study, the automated software gives promising, successful results, and the advantages of automated methods over manual analysis make it a promising tool with the potential to be successfully incorporated into anti-poaching strategies.
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影响因子：
2.3
作者：
CAUGHLEY, G
通讯作者：
CAUGHLEY, G
影响因子：
3
作者：
Baluja, Javier;Diago, Maria P.;Tardaguila, Javier
通讯作者：
Tardaguila, Javier
DOI：
10.3390/s18072244
发表时间：
2018-07-12
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
de Oliveira DC;Wehrmeister MA
通讯作者：
Wehrmeister MA
影响因子：
1.5
作者：
Chretien, Louis-Philippe;Theau, Jerome;Menard, Patrick
通讯作者：
Menard, Patrick
影响因子：
2.3
作者：
Kirkman, Steve P.;Yemane, D.;Underhill, L. G.
通讯作者：
Underhill, L. G.