Runway Intrusion Detection Using Deep Learning
Abstract
A large number of people use airports intensively for travel and transportation. However, the excess number and the traffic density can cause potential crimes at airports. With the development of technology and security management strategies, the possibility of accidents and attacks is reduced by increasing safety measures. For such reason, security systems are used to monitor the terminal and passengers and reveal any vulnerabilities. The incidents that develop or are likely to develop at airports are detected by these systems. Applications such as border violations and tracking, and prevention attempting to enter the runway during takeoff or landing are carried out around the airport. In this study, early warning systems, which are a critical issue for airport security, were examined. Focusing on object detection, image processing methods by the YOLOv10 model are introduced to prevent threats around the airport. Imaginary border violation scenarios determined by object detection and classification were evaluated. As a result, the classification success rates for tree, human, and animal (cat) were determined as 99%, 94%, and 100% for the testing dataset.
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