Improving Pallet Detection Using Synthetic Data

TL;DR

This paper demonstrates that synthetic data, especially Unity-generated, can significantly improve pallet detection accuracy in warehouse environments, though lighting conditions greatly affect performance.

Contribution

It introduces a method using domain-randomised and Unity-generated synthetic data for pallet instance segmentation, with performance improvements over previous approaches.

Findings

69% and 50% mAP50 improvements on real data for stacked and racked pallets

Performance drops significantly under darker lighting conditions, up to 80% brightness reduction

Two-stage detector with YOLOv8 and SAM showed unstable performance

Abstract

The use of synthetic data in machine learning saves a significant amount of time when implementing an effective object detector. However, there is limited research in this domain. This study aims to improve upon previously applied implementations in the task of instance segmentation of pallets in a warehouse environment. This study proposes using synthetically generated domain-randomised data as well as data generated through Unity to achieve this. This study achieved performance improvements on the stacked and racked pallet categories by 69% and 50% mAP50, respectively when being evaluated on real data. Additionally, it was found that there was a considerable impact on the performance of a model when it was evaluated against images in a darker environment, dropping as low as 3% mAP50 when being evaluated on images with an 80% brightness reduction. This study also created a two-stage detector that used YOLOv8 and SAM, but this proved to have unstable performance. The use of domain-randomised data proved to have negligible performance improvements when compared to the Unity-generated data.