ArticleScientific reports2017
Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Wildlife Responses to Drone Noise: A Preliminary Approach for Quantifying Disturbance During Single- and Dual-Drone Flights.Ecology and evolution · 2026Article
- Uncovering Hidden Predators: Thermal Drone Detection of Antarctic Fur Seals in Tussac Grass at South Georgia.Ecology and evolution · 2026Article
- WildDrone: autonomous drone technology for monitoring wildlife populations.Frontiers in robotics and AI · 2025Article
- Article
- Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery.Sensors (Basel, Switzerland) · 2024Article
- Evaluating Thermal Infrared Drone Flight Parameters on Spider Monkey Detection in Tropical Forests.Sensors (Basel, Switzerland) · 2024Article
- Aerial Wildlife Image Repository for animal monitoring with drones in the age of artificial intelligence.Database : the journal of biological databases and curation · 2024Article
- Airborne imagery does not preclude detectability issues in estimating bird colony size.Scientific reports · 2024Article
- Utility of Spectral Filtering to Improve the Reliability of Marine Fauna Detections from Drone-Based Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys.Scientific reports · 2023Article
- The broad scale impact of climate change on planning aerial wildlife surveys with drone-based thermal cameras.Scientific reports · 2023Article
- Experimental verification of seafloor crustal deformation observations by UAV-based GNSS-A.Scientific reports · 2023Article
- Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys.Scientific reports · 2023Article
- Using piecewise regression to identify biological phenomena in biotelemetry datasets.The Journal of animal ecology · 2022Article
- Robust Algorithms for Drone-Assisted Monitoring of Big Animals in Harsh Conditions of Siberian Winter Forests: Recovery of European elk (Animals : an open access journal from MDPI · 2022Article
- Evaluating the effectiveness of drones for quantifying invasive upside-down jellyfish (Cassiopea sp.) in Lake Macquarie, Australia.PloS one · 2022Article
- Review
- The behavioral responses of a nocturnal burrowing marsupial (Ecology and evolution · 2021Article
- Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks.Sensors (Basel, Switzerland) · 2021Article
- An Evaluation of the Factors Affecting 'Poacher' Detection with Drones and the Efficacy of Machine-Learning for Detection.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Estimating animal populations is critical for wildlife management. Aerial surveys are used for generating population estimates, but can be hampered by cost, logistical complexity, and human risk. Additionally, human counts of organisms in aerial imagery can be tedious and subjective. Automated approaches show promise, but can be constrained by long setup times and difficulty discriminating animals in aggregations. We combine unmanned aircraft systems (UAS), thermal imagery and computer vision to improve traditional wildlife survey methods. During spring 2015, we flew fixed-wing UAS equipped with thermal sensors, imaging two grey seal (Halichoerus grypus) breeding colonies in eastern Canada. Human analysts counted and classified individual seals in imagery manually. Concurrently, an automated classification and detection algorithm discriminated seals based upon temperature, size, and shape of thermal signatures. Automated counts were within 95-98% of human estimates; at Saddle Island, the model estimated 894 seals compared to analyst counts of 913, and at Hay Island estimated 2188 seals compared to analysts' 2311. The algorithm improves upon shortcomings of computer vision by effectively recognizing seals in aggregations while keeping model setup time minimal. Our study illustrates how UAS, thermal imagery, and automated detection can be combined to efficiently collect population data critical to wildlife management.
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