Applying FastPhotoStyle to Synthetic Data for Military Vehicle Detection
| Autor: | Yang Hunmin, Kyungmin Lee, Se-Yoon Oh, Hyeongkeun Lee |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science Deep learning 020208 electrical & electronic engineering 02 engineering and technology computer.software_genre Object detection Synthetic data Domain (software engineering) Visualization 020901 industrial engineering & automation Data visualization Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Task analysis Artificial intelligence Data mining business computer |
| Zdroj: | 2020 20th International Conference on Control, Automation and Systems (ICCAS). |
| Popis: | Object detection is one of the main task for the deep learning applications. Deep learning performance has already exceeded human’s detection ability, in the case when there are lots of data for training deep neural networks. In the case of military fields, there are needs to resolve the data shortage problem to employ deep learning system efficiently with benefits. Generating the synthetic data can be a solution, but the domain gap between the synthetic and real data is still an obstacle for training the model. In this paper, we propose a method for decreasing the domain gap by applying style transfer techniques to synthetic data for military vehicle detection. Utilizing FastPhotoStyle to the synthetic data aids efficiently improving the accuracy of object detection when the real data is insufficiency for training. Specifically, we show that stylization which enables artificial data more realistic diminishes the domain gap by evaluating the visualization of their distributions using principal component analysis and Frechet inception distance score. As a result, the performance has been improved about 8% in the AP@50 metric for stylized synthetic data. |
| Databáze: | OpenAIRE |
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