Observing Individuals and Behavior of Hainan Gibbons (Nomascus hainanus) Using Drone Infrared and Visible Image Fusion Technology

Li, Shengshi; Wang, Guanjun; Zhang, Hui; Zou, Yonghua

doi:10.3390/drones7090543

Cited by 3 publications

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This enables researchers to study tigers in their own environments while minimizing any disruption (Kamran et al, 2021). Furthermore, the unobtrusive characteristic of drones minimizes anxiety in tigers and guarantees the preservation of their innate habits without disturbance (Li et al, 2023). This tackles a prevalent issue linked to conventional land-based tracking techniques.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Drone Technology for Tracking of Tigers

AlZubi,

Alkhanifer

2024

IJAR

View full text Add to dashboard Cite

Background: Tigers, as iconic apex predators and symbols of biodiversity conservation, face numerous threats to their existence. Effective tracking and monitoring are essential for understanding and preserving these majestic creatures and their habitats. The convergence of machine learning and drone technology has emerged as transformative tools in the field of tiger tracking. Drones, or Unmanned Aerial Vehicles (UAVs), have rapidly become invaluable assets in wildlife conservation. Machine learning algorithms, with their capacity to analyze complex datasets, make predictions and automate decision-making processes, offer a novel approach to processing the massive amounts of data generated by drones, including images, sounds and sensor readings. Methods: This paper explores the historical significance of tiger tracking, the pivotal role of drones in conservation and the transformative capabilities of machine learning in wildlife monitoring. In this work, an accurate framework for tiger detection based on YOLOv8 is utilized. Result: By examining the interplay between machine learning, drone technology and tiger conservation, this paper highlights the potential for innovation and the challenges that lie ahead, promising a brighter future for these iconic creatures and their ecosystems. The fine-tuned YOLOv8 model demonstrates exceptional object detection performance, boasting a mAP50 of 0.9820 and a mAP50-95 of 0.6856, coupled with precise classification (precision 0.9646) and robust instance capture (recall 0.9580).

show abstract

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Drone Technology for Tracking of Tigers

AlZubi,

Alkhanifer

2024

IJAR

View full text Add to dashboard Cite

show abstract

Population Viability and Genetic Structure of the Last Remaining Population of the Critically Endangered Gray Snub-Nosed Monkey (Rhinopithecus brelichi)

Guo,

Garber,

Ping

et al. 2024

Int J Primatol

View full text Add to dashboard Cite

Vegetation Type Preferences in Red Deer (Cervus elaphus) Determined by Object Detection Models

Fugl,

Jensen,

Korsgaard

et al. 2024

Drones

View full text Add to dashboard Cite

This study investigates the possibility of utilising a drone equipped with a thermal camera to monitor the spatial distribution of red deer (Cervus elaphus) and to determine their behavioural patterns, as well as preferences for vegetation types in a moor in Denmark. The spatial distribution of red deer was mapped according to time of day and vegetation types. Reed deer were separated manually from fallow deer (Dama dama) due to varying footage quality. Automated object detection from thermal camera footage was used to identification of two behaviours, “Eating” and “Lying”, enabling insights into the behavioural patterns of red deer in different vegetation types. The results showed a migration of red deer from the moors to agricultural fields during the night. The higher proportion of time spent eating in agricultural grass fields compared to two natural vegetation types, “Grey dune” and “Decalcified fixed dune”, indicates that fields are important foraging habitats for red deer. The red deer populations were observed significantly later on grass fields compared to the natural vegetation types. This may be due to human disturbance or lack of randomisation of the flight time with the drone. Further studies are suggested across different seasons as well as the time of day for a better understanding of the annual and diurnal foraging patterns of red deer.

show abstract

Observing Individuals and Behavior of Hainan Gibbons (Nomascus hainanus) Using Drone Infrared and Visible Image Fusion Technology

Cited by 3 publications

References 50 publications

Application of Machine Learning in Drone Technology for Tracking of Tigers

Application of Machine Learning in Drone Technology for Tracking of Tigers

Population Viability and Genetic Structure of the Last Remaining Population of the Critically Endangered Gray Snub-Nosed Monkey (Rhinopithecus brelichi)

Vegetation Type Preferences in Red Deer (Cervus elaphus) Determined by Object Detection Models

Contact Info

Product

Resources

About