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.

使用合成数据在机器学习中能够节省大量时间，本研究旨在改进先前实施的方法，以实现仓库环境中托盘的实例分割。通过使用合成生成的域随机数据和通过 Unity 生成的数据，该研究在真实数据评估中对叠放和垛放托盘类别分别实现了 69% 和 50% mAP50 的性能提升。此外，在光线较暗的环境下评估模型时，其性能有显著影响，当在亮度降低 80% 的图像上进行评估时，mAP50 降至 3% 左右。此研究还创建了一个使用 YOLOv8 和 SAM 的两阶段检测器，但其性能不稳定。与 Unity 生成数据相比，使用域随机数据的性能改进可忽略。