This paper is a study on data augmentation for small dataset by using deep learning. In case of training a deep learning model for recognition and classification of non-mainstream objects, there is a limit to obtaining a large amount of training data. Therefore, this paper proposes a data augmentation method using perspective transform and image synthesis. In addition, it is necessary to save the object area for all training data to detect the object area. Thus, we devised a way to augment the data and save object regions at the same time. To verify the performance of the augmented data using the proposed method, an experiment was conducted to compare classification accuracy with the augmented data by the traditional method, and transfer learning was used in model learning. As experimental results, the model trained using the proposed method showed higher accuracy than the model trained using the traditional method.

Abstract
1. 서 론
2. 제안하는 방법
3. 실험 및 결과
4. 결론 및 향후 연구
References

• 김진용(Dept. of Electrical and Computer Engineering, Pusan National University) | Jin Yong Kim
• 김은경(Dept. of Electrical and Computer Engineering, Pusan National University) | Eun Kyeong Kim
• 김성신(School of Electrical and Computer Engineering, Pusan National University) | Sungshin Kim Corresponding author