The development of high-performing object detection models requires extensive and varied datasets with accurately annotated images, a process that is traditionally labor-intensive and prone to errors. To address these challenges, this report explores the generation of synthetic data using domain randomization techniques to train object detection models. We present a pipeline that integrates synthetic data creation in Unity, and the training of YOLOv8 object detection models. Our approach uses the Unity Perception package to produce diverse and precisely annotated datasets, overcoming the domain gap typically associated with synthetic data. The pipeline was evaluated through a series of experiments, analyzing the impact of various parameters such as background textures, and training arguments on model performance. The results demonstrate that models trained with our synthetic data can achieve high accuracy and generalize well to real-world scenarios, offering a scalable and efficient alternative to manual data annotation.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-532894 |
| Date | January 2024 |
| Creators | Arnestrand, Hampus, Mark, Casper |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | MATVET-F ; 24002 |
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