Advanced Computer Vision Methods for Avian Risk Assessment Using Multispectral Drones
The growing threat of the Avian Influenza virus has raised the alarm for conservationists, ecologists and researchers to develop innovative solutions to monitor wildlife birds using drones and early diagnosing through advanced image processing methods. In recent years, outbreaks of the Avian Influen...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11107416/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849392655247605760 |
|---|---|
| author | Dimitrios Mpouziotas Petros Karvelis Ioulia Kapsali Chrysostomos Stylios Vasilios Tsiouris |
| author_facet | Dimitrios Mpouziotas Petros Karvelis Ioulia Kapsali Chrysostomos Stylios Vasilios Tsiouris |
| author_sort | Dimitrios Mpouziotas |
| collection | DOAJ |
| description | The growing threat of the Avian Influenza virus has raised the alarm for conservationists, ecologists and researchers to develop innovative solutions to monitor wildlife birds using drones and early diagnosing through advanced image processing methods. In recent years, outbreaks of the Avian Influenza virus have devastated avian populations worldwide, leading to significant declines in bird colonies, particularly in vulnerable species such as the Dalmatian Pelicans (Pelecanus crispus). These significant changes to the environment may contribute to a Cascade effect impacting the food chain of both predators and preys. This study’s goal is to monitor wildlife birds in the Gulf of Amvrakikos and build an intelligent system that utilizes images derived from drones equipped with thermal imagery. This system leverages state-of-the-art computer vision techniques with spatial analysis characteristics to analyze wildlife bird behavior through optical cameras and extract features for each bird. Coupled with data derived from thermal imagery as a crucial factor, it can perform a comprehensive risk assessment by correlating thermal patterns with behavioral insights, utilizing the Analytic Hierarchy Process (AHP) method. This study pioneers a novel methodology for avian health assessment using drones and identifies abnormal patterns to detect the presence of avian influenza. |
| format | Article |
| id | doaj-art-d51f6c30eeb94cf490b5fdabf1ac6c2c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d51f6c30eeb94cf490b5fdabf1ac6c2c2025-08-20T03:40:43ZengIEEEIEEE Access2169-35362025-01-011313721913723910.1109/ACCESS.2025.359538911107416Advanced Computer Vision Methods for Avian Risk Assessment Using Multispectral DronesDimitrios Mpouziotas0https://orcid.org/0009-0005-1628-767XPetros Karvelis1https://orcid.org/0000-0002-0483-4868Ioulia Kapsali2Chrysostomos Stylios3https://orcid.org/0000-0002-2888-6515Vasilios Tsiouris4https://orcid.org/0000-0003-2970-2463Industrial Systems Institute, Athena RC, Patra, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, Ioannina, GreeceIndustrial Systems Institute, Athena RC, Patra, GreeceIndustrial Systems Institute, Athena RC, Patra, GreeceUnit of Avian Medicine, Clinic of Farm Animals, School of Veterinary Medicine, Aristotle University of Thessaloniki, Thessaloniki, GreeceThe growing threat of the Avian Influenza virus has raised the alarm for conservationists, ecologists and researchers to develop innovative solutions to monitor wildlife birds using drones and early diagnosing through advanced image processing methods. In recent years, outbreaks of the Avian Influenza virus have devastated avian populations worldwide, leading to significant declines in bird colonies, particularly in vulnerable species such as the Dalmatian Pelicans (Pelecanus crispus). These significant changes to the environment may contribute to a Cascade effect impacting the food chain of both predators and preys. This study’s goal is to monitor wildlife birds in the Gulf of Amvrakikos and build an intelligent system that utilizes images derived from drones equipped with thermal imagery. This system leverages state-of-the-art computer vision techniques with spatial analysis characteristics to analyze wildlife bird behavior through optical cameras and extract features for each bird. Coupled with data derived from thermal imagery as a crucial factor, it can perform a comprehensive risk assessment by correlating thermal patterns with behavioral insights, utilizing the Analytic Hierarchy Process (AHP) method. This study pioneers a novel methodology for avian health assessment using drones and identifies abnormal patterns to detect the presence of avian influenza.https://ieeexplore.ieee.org/document/11107416/Computer visionwildlife bird monitoringenvironmental assessmentavian influenzathermal imageryavian risk assessment |
| spellingShingle | Dimitrios Mpouziotas Petros Karvelis Ioulia Kapsali Chrysostomos Stylios Vasilios Tsiouris Advanced Computer Vision Methods for Avian Risk Assessment Using Multispectral Drones IEEE Access Computer vision wildlife bird monitoring environmental assessment avian influenza thermal imagery avian risk assessment |
| title | Advanced Computer Vision Methods for Avian Risk Assessment Using Multispectral Drones |
| title_full | Advanced Computer Vision Methods for Avian Risk Assessment Using Multispectral Drones |
| title_fullStr | Advanced Computer Vision Methods for Avian Risk Assessment Using Multispectral Drones |
| title_full_unstemmed | Advanced Computer Vision Methods for Avian Risk Assessment Using Multispectral Drones |
| title_short | Advanced Computer Vision Methods for Avian Risk Assessment Using Multispectral Drones |
| title_sort | advanced computer vision methods for avian risk assessment using multispectral drones |
| topic | Computer vision wildlife bird monitoring environmental assessment avian influenza thermal imagery avian risk assessment |
| url | https://ieeexplore.ieee.org/document/11107416/ |
| work_keys_str_mv | AT dimitriosmpouziotas advancedcomputervisionmethodsforavianriskassessmentusingmultispectraldrones AT petroskarvelis advancedcomputervisionmethodsforavianriskassessmentusingmultispectraldrones AT iouliakapsali advancedcomputervisionmethodsforavianriskassessmentusingmultispectraldrones AT chrysostomosstylios advancedcomputervisionmethodsforavianriskassessmentusingmultispectraldrones AT vasiliostsiouris advancedcomputervisionmethodsforavianriskassessmentusingmultispectraldrones |