Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Wildlife-Road Interactions and Conservation · Species Distribution and Climate Change · Wildlife Ecology and Conservation
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Informatics and Telecommunications, University of Ioannina, 451 10 Ioannina, Greece
Industrial Systems Institute, Athena RC, 263 31 Patra, Greece
Funding
Green Fund
References (36)
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