Deep Learning Algorithm Training and Performance Analysis for Corridor Monitoring

Authors

  • Woo-Jin Jung Student, Department of Avionics, Hanseo University, Seosan 3162, Korea
  • Won-Hyuck Choi Student, Department of Avionics, Hanseo University, Seosan 3162, Korea

Keywords:

Urban Air Mobility, Advanced Air Mobility, Object Detection Algorithms, YOLO

Abstract

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.

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Published

27.12.2023

How to Cite

Jung, W.-J. ., & Choi, W.-H. . (2023). Deep Learning Algorithm Training and Performance Analysis for Corridor Monitoring. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 452–456. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4339

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

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