Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques
Wildlife conservationists have traditionally relied on manual identification and tracking of bird species to monitor populations and identify potential threats. However, many of these techniques may prove to be time-consuming. With the advancement of computer vision techniques, automated bird detect...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/13/7787 |
| _version_ | 1797592036987109376 |
|---|---|
| author | Dimitrios Mpouziotas Petros Karvelis Ioannis Tsoulos Chrysostomos Stylios |
| author_facet | Dimitrios Mpouziotas Petros Karvelis Ioannis Tsoulos Chrysostomos Stylios |
| author_sort | Dimitrios Mpouziotas |
| collection | DOAJ |
| description | Wildlife conservationists have traditionally relied on manual identification and tracking of bird species to monitor populations and identify potential threats. However, many of these techniques may prove to be time-consuming. With the advancement of computer vision techniques, automated bird detection and recognition have become possible. In this manuscript, we present an application of an object-detection model for identifying and tracking wild bird species in natural environments. We used a dataset of bird images captured in the wild and trained the YOLOv4 model to detect bird species with high accuracy. We evaluated the model’s performance on a separate set of test images and achieved an average precision of 91.28%. Our method surpassed the time-consuming nature of manual identification and tracking, allowing for efficient and precise monitoring of bird populations. Through extensive evaluation on a separate set of test images, we demonstrated the performance of our model. Furthermore, our results demonstrated the potential of using YOLOv4 for automated bird detection and monitoring in the wild, which could help conservationists better understand bird populations and identify potential threats. |
| first_indexed | 2024-03-11T01:46:45Z |
| format | Article |
| id | doaj.art-12553d7b7e70498d8ee064c825b5c685 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:46:45Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-12553d7b7e70498d8ee064c825b5c6852023-11-18T16:11:27ZengMDPI AGApplied Sciences2076-34172023-06-011313778710.3390/app13137787Automated Wildlife Bird Detection from Drone Footage Using Computer Vision TechniquesDimitrios Mpouziotas0Petros Karvelis1Ioannis Tsoulos2Chrysostomos Stylios3Department of Informatics and Telecommunications, University of Ioannina, 451 10 Ioannina, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 451 10 Ioannina, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 451 10 Ioannina, GreeceIndustrial Systems Institute, Athena RC, 263 31 Patra, GreeceWildlife conservationists have traditionally relied on manual identification and tracking of bird species to monitor populations and identify potential threats. However, many of these techniques may prove to be time-consuming. With the advancement of computer vision techniques, automated bird detection and recognition have become possible. In this manuscript, we present an application of an object-detection model for identifying and tracking wild bird species in natural environments. We used a dataset of bird images captured in the wild and trained the YOLOv4 model to detect bird species with high accuracy. We evaluated the model’s performance on a separate set of test images and achieved an average precision of 91.28%. Our method surpassed the time-consuming nature of manual identification and tracking, allowing for efficient and precise monitoring of bird populations. Through extensive evaluation on a separate set of test images, we demonstrated the performance of our model. Furthermore, our results demonstrated the potential of using YOLOv4 for automated bird detection and monitoring in the wild, which could help conservationists better understand bird populations and identify potential threats.https://www.mdpi.com/2076-3417/13/13/7787bird detectioncomputer visionYOLO DarknetDarkSuite |
| spellingShingle | Dimitrios Mpouziotas Petros Karvelis Ioannis Tsoulos Chrysostomos Stylios Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques Applied Sciences bird detection computer vision YOLO Darknet DarkSuite |
| title | Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques |
| title_full | Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques |
| title_fullStr | Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques |
| title_full_unstemmed | Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques |
| title_short | Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques |
| title_sort | automated wildlife bird detection from drone footage using computer vision techniques |
| topic | bird detection computer vision YOLO Darknet DarkSuite |
| url | https://www.mdpi.com/2076-3417/13/13/7787 |
| work_keys_str_mv | AT dimitriosmpouziotas automatedwildlifebirddetectionfromdronefootageusingcomputervisiontechniques AT petroskarvelis automatedwildlifebirddetectionfromdronefootageusingcomputervisiontechniques AT ioannistsoulos automatedwildlifebirddetectionfromdronefootageusingcomputervisiontechniques AT chrysostomosstylios automatedwildlifebirddetectionfromdronefootageusingcomputervisiontechniques |