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Deep neural network shows excellent use in a lot of real-world tasks. One ofthe deep learning tasks is object detection. Well-annotated datasets willaffect deep neural network accuracy. More data learned by deep neural networkswill make the model more accurate. However, a well-annotated dataset is hard tofind, especially in a specific domain. To overcome this, computer-generateddata or virtual datasets are used. Researchers could generate many images withspecific use cases also with its annotation. Research studies showed thatvirtual datasets could be used for object detection tasks. Nevertheless, withthe usage of the virtual dataset, the model must adapt to real datasets, or themodel must have domain adaptability features. We explored the domain adaptationinside the object detection model using a virtual dataset to overcome a fewwell-annotated datasets. We use VW-PPE dataset, using 5000 and 10000 virtualdata and 220 real data. For model architecture, we used YOLOv4 usingCSPDarknet53 as the backbone and PAN as the neck. The domain adaptationtechnique with fine-tuning only on backbone weight achieved a mean averageprecision of 74.457%.

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