Detection of Small Birds in Flight Pathways

Bird strikes pose a significant risk to aviation safety, particularly during takeoff and landing, when aircraft are most vulnerable. Our project develops a cutting-edge artificial intelligence system using the YOLOv8 model to address this critical issue. The system is focused on detecting and calculating the distance of birds within airport environments and along flight paths. By training the model with a comprehensive dataset, our technology achieves exceptional accuracy in real-time bird detection. Integrated with webcam technology, it provides instant, actionable insights into avian activity, enhancing safety measures in and around airfields. This innovation not only promises to revolutionize the way aviation personnel monitor and manage airspace for safety but also paves the way for implementing AI-based solutions in mitigating natural hazards in aviation. Our project underscores the potential of integrating machine learning with aviation operations to proactively address safety concerns, ensuring a safer flying experience. Through this technological advancement, we aim to set new benchmarks in aviation safety, offering a model that could be adapted across global aviation sectors to safeguard against the perennial challenge of bird strikes.