# Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques
Image frame location at Koronisia, Nisida Kamakia (location at Koronisia, Nisida Kamakia (GPS Pin)), received on 23 March 2023.  
using a diverse dataset of drone-captured imagery, encompassing different environments, lighting conditions, and bird species. Through this research, we aimed to contribute to the development of efficient and reliable tools for wildlife bird monitoring, which can enhance our understanding of avian populations, support conservation efforts, and inform management decisions. The utilisation of computer vision methods for wildlife bird detection from drone footage has significant implications for ecological research and conservation. Firstly, it enables researchers to conduct large-scale bird surveys in a cost-effective and non-invasive manner. Drones can cover vast areas of land, allowing for the monitoring of bird populations in remote and inaccessible regions, as well as areas with challenging terrain. This expanded spatial coverage provides valuable insights into bird distributions, habitat preferences, and migratory patterns, which were previously difficult to obtain. Moreover, the automated and standardised nature of computer vision algorithms ensures consistency in data processing and analysis. Manual bird surveys often suffer from inter-observer variability and subjectivity, leading to inconsistencies in data collection and interpretation. By employing computer vision methods, we can reduce these biases and increase the reliability and reproducibility of bird-monitoring efforts. Consistent and standardised data collection is crucial for accurate trend analysis, population assessments, and the identification of critical conservation areas. Finally, we highlight the advantages of our method in improving the efficiency, accuracy, non-invasiveness, replicability, and potential real-time monitoring capabilities for bird detection and conservation efforts. By leveraging deep learning techniques, the proposed methodology significantly improves the efficiency of bird detection compared to traditional manual methods. It eliminates the need for labour-intensive and time-consuming manual identification and tracking processes, allowing for rapid and automated analysis of bird populations. Next, the automatic-bird-detection approach enables non-intrusive monitoring of bird populations in their natural habitats. This non-invasive method minimises disturbance to the birds and reduces the potential of altering their behaviours, providing more reliable and ecologically meaningful data for conservation efforts. Finally, using lighter versions of the YOLO method (tinyYOLO [10]), we proved that our methodology has the potential for real-time bird detection and monitoring. This capability enables a timely response to emerging threats, early detection of population changes, and informed decision-making for effective conservation strategies. The choice of the tinyYOLO method instead of other popular methods, e.g., MobileSSD [11], which is generally more lightweight and compact compared to tinyYOLO, is that it is suitable for resource-constrained environments or devices with limited computational power. MobileNetSSD and tinyYOLO differ in their underlying network architectures. MobileNetSSD employs depthwise separable convolutions, which enable more efficient use of parameters and reduce computation. In contrast, tinyYOLO utilises a smaller network architecture with multiple detection layers to achieve good accuracy.