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.

翻译：在机器学习中使用合成数据可以显著节省实现高效目标检测器的时间。然而，该领域的研究仍较为有限。本研究旨在改进以往在仓库环境中托盘实例分割任务中的实现方法。研究提出使用合成生成的域随机化数据以及通过Unity生成的数据来实现这一目标。在真实数据评估中，堆叠托盘和货架托盘类别的性能分别提升了69%和50%的mAP50。此外，研究发现当模型在较暗环境下评估时，性能受到显著影响，在亮度降低80%的图像上评估时mAP50下降至3%。本研究还创建了一个使用YOLOv8和SAM的两阶段检测器，但其性能表现不稳定。与Unity生成的数据相比，域随机化数据的性能改进微乎其微。