# Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques  
[PERSON]  
1 Department of Informatics and Telecommunications, University of Ioannina, 451 10 Ioannina, Greece; [EMAIL_ADDRESS] (D.M. ); [EMAIL_ADDRESS] (P.K.) [PERSON]  
1 Department of Informatics and Telecommunications, University of Ioannina, 451 10 Ioannina, Greece; [EMAIL_ADDRESS] (D.M. ); [EMAIL_ADDRESS] (P.K.) [PERSON]  
1 Department of Informatics and Telecommunications, University of Ioannina, 451 10 Ioannina, Greece; [EMAIL_ADDRESS] (D.M. ); [EMAIL_ADDRESS] (P.K.) [PERSON]  
2 Industrial Systems Institute, Athena RC, 263 31 Patra, Greece; [EMAIL_ADDRESS] 2  
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surveillance in challenging and hard-to-access environments, as depicted in Figure 1. For example, drones equipped with thermal or multispectral sensors can detect subtle changes in temperature or vegetation that are indicative of animal presence or habitat changes. This can provide valuable insights for wildlife management and conservation, as it allows for targeted interventions and informed decision-making. Computer vision [5] encompasses a broad range of techniques that aim to enable machines to interpret and understand visual information. Within the field of wildlife monitoring, computer vision algorithms can be applied to analyse drone footage and automatically detect, track, and classify bird species based on their visual characteristics. These algorithms leverage deep learning [6; 7] methods, which have shown remarkable performance in various image recognition tasks. One popular computer vision method for object detection is the family of convolutional neural networks (CNNs) [6]. CNNs are deep learning architectures specifically designed to extract and learn meaningful features from images. They have been successfully employed in numerous domains, including object recognition, face detection, and scene understanding. Recent advances in CNN-based methods such as the You Only Look Once (YOLO) algorithm [8] have shown promising results in bird detection tasks. Applying computer vision methods to wildlife bird detection from drone footage offers several advantages. Firstly, it allows for rapid and automated analysis of large-scale datasets, enabling researchers to process and extract valuable information more efficiently. Secondly, computer vision algorithms can handle the challenges posed by the small size and swift movements of birds, making them well suited for accurately detecting and identifying bird species. Additionally, the integration of computer vision techniques with drone technology provides an opportunity for real-time monitoring and decision-making, facilitating timely conservation interventions and adaptive management strategies. Overall, convolutional neural network algorithms, including YOLO, have shown great promise for bird detection and identification in drone footage. These algorithms have the potential to greatly improve our understanding of bird populations and their behaviour and contribute to conservation efforts and ecological studies [9]. In this manuscript, we present a comprehensive study of the application of computer vision methods for wildlife bird detection from drone footage. Our aim was to assess the performance and effectiveness of various computer vision algorithms, such as the YOLO method, for the accurate detection of bird species. We evaluated the algorithms  
Figure 1: Image example of a bird’s eye view of wildlife and flocks of birds retrieved using drone footage.