用合成数据+几何特征，实现高精度托盘检测与定位，免人工标注。
Pallet Detection And Localisation From Synthetic Data
- 纯合成数据训练，结合Unity域随机化，无需人工标注。
- 单托盘检测mAP50达0.995，5米内定位误差<4.2cm，旋转误差8.2°。
- 适合需要快速部署、低成本标注的仓储自动化场景。
全球仓储行业正快速发展，预计2024至2030年市场年均增长8.1% [Grand View Research, 2021]。这一扩张推动了高效托盘检测与定位系统的需求。尽管自动化可显著提升仓库效率，但传统计算机视觉项目通常需耗时35秒/图像进行人工标注。本文提出一种新方法，仅使用合成数据和基于侧面几何特征的算法，实现托盘检测与定位。通过在Unity中构建域随机化引擎，完全避免了繁琐的人工标注过程，并在真实世界数据集上取得了0.995 mAP50的单托盘检测性能。同时，在5米范围内头对头放置的托盘上，平均位置精度低于4.2厘米，平均旋转精度为8.2°。
原文摘要 · Abstract (English)
The global warehousing industry is experiencing rapid growth, with the market size projected to grow at an annual rate of 8.1% from 2024 to 2030 [Grand View Research, 2021]. This expansion has led to a surge in demand for efficient pallet detection and localisation systems. While automation can significantly streamline warehouse operations, the development of such systems often requires extensive manual data annotation, with an average of 35 seconds per image, for a typical computer vision project. This paper presents a novel approach to enhance pallet detection and localisation using purely synthetic data and geometric features derived from their side faces. By implementing a domain randomisation engine in Unity, the need for time-consuming manual annotation is eliminated while achieving high-performance results. The proposed method demonstrates a pallet detection performance of 0.995 mAP50 for single pallets on a real-world dataset. Additionally, an average position accuracy of less than 4.2 cm and an average rotation accuracy of 8.2° were achieved for pallets within a 5-meter range, with the pallet positioned head-on.
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