Improving Pallet Detection Using Synthetic Data
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 28 references that are in the map. First posted on arXiv 11 Feb 2024.
Builds on
19 of 28 references.
References · 19
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7 more
- 2019Synthetic Data for Deep Learningcited by 113
- 2021A Survey of Modern Deep Learning based Object Detection Modelscited by 97
- 2021Unity Perception: Generate Synthetic Data for Computer Visioncited by 43
- 2016UnrealCV: Connecting Computer Vision to Unreal Enginecited by 41
- 2018Detection, localisation and tracking of pallets using machine learning techniques and 2D range datacited by 10
- 2016UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Visioncited by 8
- 2023Pallet Detection from Synthetic Data Using Game Enginescited by 2
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Citing papers per year: 2025: 2.
2 papers.