# Supervised Virtual-to-Real Domain Adaptation for Object Detection Task using YOLO

arXiv: https://arxiv.org/abs/2302.13891 · HTML: https://mlsanity.com/paper/2302.13891/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 22 references that are in the map.
- First posted on arXiv 27 Feb 2023.

## Builds on (most cited first, 16 of 16)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://mlsanity.com/paper/1911.11929/) (2019)
- [Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?](https://mlsanity.com/paper/1610.01983/) (2016)
- [Learning to Detect and Track Visible and Occluded Body Joints in a Virtual World](https://mlsanity.com/paper/1803.08319/) (2018)
- [UnrealCV: Connecting Computer Vision to Unreal Engine](https://mlsanity.com/paper/1609.01326/) (2016)
- [Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars](https://mlsanity.com/paper/1712.01397/) (2017)
- [Understanding deep features with computer-generated imagery](https://mlsanity.com/paper/1506.01151/) (2015)
- [Semi-Supervised Object Detection with Sparsely Annotated Dataset](https://mlsanity.com/paper/2006.11692/) (2020)

## Abstract (arXiv)

Deep neural network shows excellent use in a lot of real-world tasks. One of the deep learning tasks is object detection. Well-annotated datasets will affect deep neural network accuracy. More data learned by deep neural networks will make the model more accurate. However, a well-annotated dataset is hard to find, especially in a specific domain. To overcome this, computer-generated data or virtual datasets are used. Researchers could generate many images with specific use cases also with its annotation. Research studies showed that virtual datasets could be used for object detection tasks. Nevertheless, with the usage of the virtual dataset, the model must adapt to real datasets, or the model must have domain adaptability features. We explored the domain adaptation inside the object detection model using a virtual dataset to overcome a few well-annotated datasets. We use VW-PPE dataset, using 5000 and 10000 virtual data and 220 real data. For model architecture, we used YOLOv4 using CSPDarknet53 as the backbone and PAN as the neck. The domain adaptation technique with fine-tuning only on backbone weight achieved a mean average precision of 74.457%.