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Evaluating Synthetic Data for Baggage Trolley Detection in Airport Logistics

Abdeldjalil Taibi, Mohmoud Badlis, Amina Bensalem, Belkacem Zouilekh, Mohammed Brahimi

TL;DR

This work introduces a synthetic data generation pipeline based on a high-fidelity Digital Twin of Algiers International Airport using NVIDIA Omniverse, and evaluates YOLO-OBB using five training strategies: real-only, synthetic-only, linear probing, full fine-tuning, and mixed training.

Abstract

Efficient luggage trolley management is critical for reducing congestion and ensuring asset availability in modern airports. Automated detection systems face two main challenges. First, strict security and privacy regulations limit large-scale data collection. Second, existing public datasets lack the diversity, scale, and annotation quality needed to handle dense, overlapping trolley arrangements typical of real-world operations. To address these limitations, we introduce a synthetic data generation pipeline based on a high-fidelity Digital Twin of Algiers International Airport using NVIDIA Omniverse. The pipeline produces richly annotated data with oriented bounding boxes, capturing complex trolley formations, including tightly nested chains. We evaluate YOLO-OBB using five training strategies: real-only, synthetic-only, linear probing, full fine-tuning, and mixed training. This allows us to assess how synthetic data can complement limited real-world annotations. Our results show that mixed training with synthetic data and only 40 percent of real annotations matches or exceeds the full real-data baseline, achieving 0.94 mAP@50 and 0.77 mAP@50-95, while reducing annotation effort by 25 to 35 percent. Multi-seed experiments confirm strong reproducibility with a standard deviation below 0.01 on mAP@50, demonstrating the practical effectiveness of synthetic data for automated trolley detection.

Evaluating Synthetic Data for Baggage Trolley Detection in Airport Logistics

TL;DR

This work introduces a synthetic data generation pipeline based on a high-fidelity Digital Twin of Algiers International Airport using NVIDIA Omniverse, and evaluates YOLO-OBB using five training strategies: real-only, synthetic-only, linear probing, full fine-tuning, and mixed training.

Abstract

Efficient luggage trolley management is critical for reducing congestion and ensuring asset availability in modern airports. Automated detection systems face two main challenges. First, strict security and privacy regulations limit large-scale data collection. Second, existing public datasets lack the diversity, scale, and annotation quality needed to handle dense, overlapping trolley arrangements typical of real-world operations. To address these limitations, we introduce a synthetic data generation pipeline based on a high-fidelity Digital Twin of Algiers International Airport using NVIDIA Omniverse. The pipeline produces richly annotated data with oriented bounding boxes, capturing complex trolley formations, including tightly nested chains. We evaluate YOLO-OBB using five training strategies: real-only, synthetic-only, linear probing, full fine-tuning, and mixed training. This allows us to assess how synthetic data can complement limited real-world annotations. Our results show that mixed training with synthetic data and only 40 percent of real annotations matches or exceeds the full real-data baseline, achieving 0.94 mAP@50 and 0.77 mAP@50-95, while reducing annotation effort by 25 to 35 percent. Multi-seed experiments confirm strong reproducibility with a standard deviation below 0.01 on mAP@50, demonstrating the practical effectiveness of synthetic data for automated trolley detection.
Paper Structure (43 sections, 9 figures, 8 tables)

This paper contains 43 sections, 9 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Computer Vision in Airport Logistics
  4. Commercial and Industrial Solutions
  5. Approaches to Object Counting: Detection vs. Density
  6. Density Based and Point Based Methods
  7. Detection Based Methods (YOLO)
  8. Sim2Real Transfer, Domain Adaptation, and Oriented Detection
  9. Limitations of Existing Public Datasets and Cross Dataset Adaptation
  10. Dataset Description
  11. Real World Airport Trolley Dataset
  12. Data Collection
  13. Diversity and Challenges
  14. Synthetic Dataset: The Algiers Digital Twin
  15. Environment and Assets
  16. ...and 28 more sections

Figures (9)

  • Figure 1: Real-world dataset sample 1.
  • Figure 2: Real-world dataset sample 2.
  • Figure 3: Synthetic dataset sample from the Algiers Digital Twin.
  • Figure 4: Synthetic dataset sample 2 from the Algiers Digital Twin.
  • Figure 5: The semi-automated "Human-in-the-Loop" annotation workflow.
  • ...and 4 more figures