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%.

翻译：深度神经网络在诸多实际任务中展现出卓越性能，其中目标检测是深度学习的重要应用领域之一。高质量标注数据集直接影响深度神经网络的精度，而模型学习的数据量越大，其准确性越高。然而，高质量标注数据集（尤其在特定领域）往往难以获取。为解决这一问题，计算机生成数据或虚拟数据集被广泛采用。研究者可针对特定场景生成大量图像及其对应标注。已有研究表明，虚拟数据集能够有效应用于目标检测任务。但使用虚拟数据集时，模型必须适应真实数据集，即具备域适应能力。本研究探索了基于虚拟数据集的目标检测域适应方法，以缓解高质量标注数据集匮乏的问题。我们采用VW-PPE数据集，使用5000张和10000张虚拟数据及220张真实数据进行实验。模型架构选用YOLOv4，以CSPDarknet53作为骨干网络，PAN作为颈部网络。通过仅在骨干网络权重上进行微调的域适应技术，模型平均精度均值达到74.457%。