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Drones
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Drones, Artificial Intelligence and Advanced Analytics for the Conservation of...
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Drones, Artificial Intelligence and Advanced Analytics for the Conservation of Threatened Species

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Ecology".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 6607

Special Issue Editor

Dr. Grant Hamilton

E-Mail Website
Guest Editor
Faculty of Science, School of Biology & Environmental Science, Queensland University of Technology, Brisbane, QLD, Australia
Interests: conservation; detection and abundance estimation using drones and AI; biological invasions; ecological statistics; ecological modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of drones has rapidly increased over the past 5 years to the point where they are now becoming well accepted for direct observation and data collection in conservation. There is an increasing recognition, however, that that the most powerful impacts will occur when they are used in combination with machine learning (otherwise known as artificial intelligence), and the advanced techniques that are required to analyse the data collected. As with the introduction of any new methodology, there is a need to confirm the existing analytical approaches that will continue to be applicable in a new context, and also to develop new approaches when they do not.

This Special Issue aims to gather original articles and reviews showing practical applications of remote sensing using drones and AI in conservation. In particular, it aims to show innovative uses of drones and AI in different contexts together with advances in machine learning techniques for image analysis, for a range of threatened species and environments. Development and critical evaluation of techniques for the analysis of data collected using these approaches is also encouraged to provide a coherent and holistic approach to the use of technology and analytics ion conservation.

You may choose our Joint Special Issue in Remote Sensing.

Prof. Dr. Grant Hamilton
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Machine learning
  • Artificial intelligence
  • Analytics
  • Conservation
  • High resolution imagery
  • Bushfire recovery
  • Vegetation conservation
  • Endangered species
  • Thermal
  • Lidar

Published Papers (1 paper)

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Research

12 pages, 3256 KiB  
Article
A Human-Detection Method Based on YOLOv5 and Transfer Learning Using Thermal Image Data from UAV Perspective for Surveillance System
by Aprinaldi Jasa Mantau, Irawan Widi Widayat, Jenq-Shiou Leu and Mario Köppen
Drones 2022, 6(10), 290; https://doi.org/10.3390/drones6100290 - 4 Oct 2022
Cited by 18 | Viewed by 5819
Abstract
At this time, many illegal activities are being been carried out, such as illegal mining, hunting, logging, and forest burning. These things can have a substantial negative impact on the environment. These illegal activities are increasingly rampant because of the limited number of [...] Read more.
At this time, many illegal activities are being been carried out, such as illegal mining, hunting, logging, and forest burning. These things can have a substantial negative impact on the environment. These illegal activities are increasingly rampant because of the limited number of officers and the high cost required to monitor them. One possible solution is to create a surveillance system that utilizes artificial intelligence to monitor the area. Unmanned aerial vehicles (UAV) and NVIDIA Jetson modules (general-purpose GPUs) can be inexpensive and efficient because they use few resources. The problem from the object-detection field utilizing the drone’s perspective is that the objects are relatively small compared to the observation space, and there are also illumination and environmental challenges. In this study, we will demonstrate the use of the state-of-the-art object-detection method you only look once (YOLO) v5 using a dataset of visual images taken from a UAV (RGB-image), along with thermal infrared information (TIR), to find poachers. There are seven scenario training methods that we have employed in this research with RGB and thermal infrared data to find the best model that we will deploy on the Jetson Nano module later. The experimental result shows that a new model with pre-trained model transfer learning from the MS COCO dataset can improve YOLOv5 to detect the human–object in the RGBT image dataset. Full article
(This article belongs to the Special Issue Drones, Artificial Intelligence and Advanced Analytics for the Conservation of Threatened Species)
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