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Computer Vision-Based Airport Turnaround Monitoring Using YOLOv11, Multi-Object Tracking, and Motion-Based Passenger
Nutchanon Suvittawat1, De Wen Soh1
1Information Systems Technology and Design, Singapore University of Technology and Design, Singapore 487372, Singapore.
Sensors (Basel, Switzerland)
|July 15, 2026
Summary
This study introduces a computer vision system using YOLOv11 object detection and tracking to automate airport turnaround monitoring. The pipeline analyzes video footage to create structured operational timelines, enhancing efficiency and punctuality.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Aerospace Operations
Background:
- Airport turnaround processes are critical for flight punctuality and efficiency.
- Current monitoring relies on manual methods or fragmented records, limiting real-time visibility and delay identification.
Purpose of the Study:
- To develop and evaluate a computer vision-based pipeline for automated airport turnaround monitoring.
- To extract key operational events and generate structured timelines from airport video footage.
Main Methods:
- Integration of YOLOv11 object detection, Norfair multi-object tracking, and frame differencing for motion analysis.
- Development of a labeled dataset with 11 airport object classes from Shinshu Matsumoto Airport footage.
- Application of frame differencing in specific regions of interest for activity detection (e.g., boarding, baggage handling).
Main Results:
- The YOLOv11 model achieved high detection performance (precision 0.9609, recall 0.9445, mAP50 0.9617).
- The pipeline successfully extracted object detections and motion spikes to generate Gantt charts of major turnaround activities.
- Demonstrated transformation of raw video into structured operational timelines.
Conclusions:
- The proposed YOLO-based pipeline enables automated, data-driven monitoring of airport turnaround processes.
- This technology enhances transparency and efficiency in ground handling operations.
- The system supports real-time visibility and improved identification of operational delays.
