This research sets out to assess the viability of using game engines to
generate synthetic training data for machine learning in the context of pallet
segmentation. Using synthetic data has been proven in prior research to be a
viable means of training neural networks and saves hours of manual labour due
to the reduced need for manual image annotation. Machine vision for pallet
detection can benefit from synthetic data as the industry increases the
development of autonomous warehousing technologies. As per our methodology, we
developed a tool capable of automatically generating large amounts of annotated
training data from 3D models at pixel-perfect accuracy and a much faster rate
than manual approaches. Regarding image segmentation, a Mask R-CNN pipeline was
used, which achieved an AP50 of 86% for individual pallets.

本研究旨在评估使用游戏引擎在托盘分割的机器学习中生成合成训练数据的可行性。研究开发了一款工具，可从 3D 模型以像素完美的准确性自动生成大量带注释的训练数据，并且比手动方法更快地进行。使用 Mask R-CNN 管道进行图像分割，对于单独的托盘，AP50 达到了 86％。