Evaluating Synthetic Data for Baggage Trolley Detection in Airport Logistics

TL;DR

This paper presents a synthetic data generation pipeline using a digital twin of an airport to improve baggage trolley detection, reducing annotation effort while maintaining high accuracy in real-world scenarios.

Contribution

We introduce a high-fidelity synthetic data pipeline for airport trolley detection and evaluate its effectiveness in training deep learning models with limited real data.

Findings

Synthetic data with limited real annotations achieves comparable accuracy to full real data.

Mixed training with synthetic and real data reduces annotation effort by 25-35%.

Results demonstrate high reproducibility and practical effectiveness of synthetic data.

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.