Deep Learning Algorithm Training and Performance Analysis for Corridor Monitoring - Jung, Woo-Jin; Choi, Won-Hyuck (International Journal of Intelligent Systems and Applications in Engineering, 2023)
K-UAM will be commercialized through maturity after 2035. Since the UAM corridor will be used vertically separating the existing helicopter corridor, the corridor usage is expected to increase. Therefore, a system for monitoring corridors is also needed. In recent years, object detection algorithms have developed significantly. Object detection algorithms are largely divided into one-stage model and two-stage model. In real-time detection, the two-stage model is not suitable for being too slow. One-stage models also had problems with accuracy, but they have improved performance through version upgrades. Among them, YOLO-V5 improved small image object detection performance through Mosaic. Therefore, YOLO-V5 is the most suitable algorithm for systems that require real-time monitoring of wide corridors. Therefore, this paper trains YOLO-V5 and analyzes whether it is ultimately suitable for corridor monitoring. K-UAM will be commercialized through maturity after 2035.