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Article

An Evaluation of the Factors Affecting ‘Poacher’ Detection with Drones and the Efficacy of Machine-Learning for Detection

1
Independent Researcher, Nottingham NG21 9FN, UK
2
School of Computer Science, Liverpool John Moores University, Liverpool L3 3AF, UK
3
Astrophysics Research Institute, Liverpool John Moores University, Liverpool L3 3AF, UK
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Department of Anthropology, University College London, Taviton Street, Bloomsbury, London WC1H OBW, UK
5
School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Biswajeet Pradhan
Sensors 2021, 21(12), 4074; https://doi.org/10.3390/s21124074
Received: 13 May 2021 / Revised: 9 June 2021 / Accepted: 10 June 2021 / Published: 13 June 2021
(This article belongs to the Special Issue Sensors and Artificial Intelligence for Wildlife Conservation)
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. View Full-Text
Keywords: drones; detection; camera; TIR; RGB; poachers; canopy; time of day; angle; automated drones; detection; camera; TIR; RGB; poachers; canopy; time of day; angle; automated
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MDPI and ACS Style

Doull, K.E.; Chalmers, C.; Fergus, P.; Longmore, S.; Piel, A.K.; Wich, S.A. An Evaluation of the Factors Affecting ‘Poacher’ Detection with Drones and the Efficacy of Machine-Learning for Detection. Sensors 2021, 21, 4074. https://doi.org/10.3390/s21124074

AMA Style

Doull KE, Chalmers C, Fergus P, Longmore S, Piel AK, Wich SA. An Evaluation of the Factors Affecting ‘Poacher’ Detection with Drones and the Efficacy of Machine-Learning for Detection. Sensors. 2021; 21(12):4074. https://doi.org/10.3390/s21124074

Chicago/Turabian Style

Doull, Katie E., Carl Chalmers, Paul Fergus, Steve Longmore, Alex K. Piel, and Serge A. Wich. 2021. "An Evaluation of the Factors Affecting ‘Poacher’ Detection with Drones and the Efficacy of Machine-Learning for Detection" Sensors 21, no. 12: 4074. https://doi.org/10.3390/s21124074

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