Adversarial Learning for Effective Detector Training via Synthetic Data
| Autor: | Ilya Basharov, Andrey Nikitin, Vadim Gorbachev |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Detector Training (meteorology) 02 engineering and technology Machine learning computer.software_genre Synthetic data Adversarial system 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2. :short16-1 |
| DOI: | 10.51130/graphicon-2020-2-4-16 |
| Popis: | Current neural network-based algorithms for object detection require a huge amount of training data. Creation and annotation of specific datasets for real-life applications require significant human and time resources that are not always available. This issue substantially prevents the successful deployment of AI algorithms in industrial tasks. One possible solutions is a synthesis of train images by rendering 3D models of target objects, which allows effortless automatic annotation. However, direct use of synthetic training datasets does not usually result in an increase of the algorithms’ quality on test data due to differences in data domains. In this paper, we propose the adversarial architecture and training method for a CNN-based detector, which allows the effective use of synthesized images in case of a lack of labeled real-world data. The method was successfully tested on real data and applied for the development of unmanned aerial vehicle (UAV) detection and localization system. |
| Databáze: | OpenAIRE |
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