Bird Detection in Microlight Aircraft Strip Using YOLOv8for Adventure Tourism

Microlight flying in India has given tourists an opportunity to experience the exhilaration of soaring in the sky, miles above the ground, without the high expenses of the usual flying options available. Flying birds poses significant threat to aviation safety, especially for smaller aircraft like microlight, due to the small size, bird targets are challenging to efficiently detect and identify in large-field observation. Advanced computer vision techniques have been used to detect and track birds near airfields and flight paths to reduce the risk. But in the tourism industry no such precautions are adapted with respect to microlight aircraft hence it’s a major concern to give attention. In this research, we examine the identification algorithm's use for real-time bird detection and its effects on the safety of microlight flying. The aim is to improve the capability of spotting birds near microlight aircraft by utilizing the advanced deep learning model, giving pilots timely knowledge to make wise decisions and take defensive action when required. The data has collected a wide range of bird photographs using web scraping techniques, including various species, lighting settings, and backgrounds. The findings of research highlight the importance of data gathering and analysis in aviation safety technology and show the smart computer vision system can protect both aircraft and human lives while adventure tourism.