Demonstrating Low-Cost Unmanned Aerial Vehicle for anti-Poaching

Paul, Joseph K.; Tankala, Yuvaraj; Gundepudi, Karthikay

doi:10.1109/indicon49873.2020.9342131

Cited by 5 publications

(1 citation statement)

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“…One possibility to combat this problem is the use of drones to monitor protected forest areas. Paul et al [87] aimed to detect animal poaching in environmental preservation areas using images collected by UAVs on patrol and the YOLO method. In the study presented by Xie et al [91], a transfer learning and improved Faster RCNN-based method achieved 93.7% accuracy for aerial forest fire detection using UAV imagery, outperforming traditional approaches.…”

Section: • Forest Ecosystemmentioning

confidence: 99%

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Teixeira,

Miguel,

Silva

et al. 2023

IEEE Access

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Unmanned Aerial Vehicles (UAVs) play an important role in many applications, including health, transport, telecommunications and safe and rescue operations. Their adoption can improve the speed and precision of applications when compared to traditional solutions based on handwork. The use of UAVs brings scientific and technological challenges. In this context, Machine Learning (ML) techniques provide solutions to several problems concerning the use of UAVs in civil and military applications. An increasing number of scientific papers on the use of ML in UAVs context have been published in academic journals. In this work, we present a literature review on the use of ML techniques in UAVs, outlining the most recurrent areas and the most commonly used ML techniques in UAV applications. The results reveal that applications in the areas of environment, communication and security are among the main research topics.INDEX TERMS unmanned aerial vehicle, machine learning, literature review, UAV applications, neural networks.

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Section: • Forest Ecosystemmentioning

confidence: 99%