Multi-Sensor Fusion and YOLOv5 Model for Automated Detection of Aircraft Cabin Door

This study investigated perception technology of an autonomous driving system to enable independent connection between an aircraft and a boarding bridge. GigE video sensors and solid-state lidars were installed on the cabin side of the boarding bridge, and a technology that fuses the data from these two different sensors was developed and applied. Using the fused data, a technology for identifying the aircraft door was researched using Yolo-v5, one of the feature point extractors. Yolo-v5 is a deep learning-based feature point extractor that was able to identify the door after being trained with more than 10,000 frames of images under predetermined weather and time conditions. Additionally, a parallel alignment control function was applied between the aircraft body and the cabin of the boarding bridge to increase the reliability of the aircraft door identification technology based on the fused data. To achieve this, a certain area of interest was set within the fused data so that the distance deviation to the left and right of the cabin could be calculated. Finally, to verify the research results, tests were conducted to identify aircraft doors under various environmental conditions with more than six airlines selected. Originally, the Yolo-v5 model secured 93.5% accuracy, but through this study, the detection accuracy for limited-environment aircraft doors was increased to over 95%.

@article{Weon2024,
  title     = {Multi-Sensor Fusion and YOLOv5 Model for Automated Detection of Aircraft Cabin Door},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Ihnsik Weon and Soon-Geul Lee},
  doi       = {10.14569/IJACSA.2024.0150626},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150626}
}