Earticle

초록

영어

In this paper, we investigate the comparative performance of two leading object detection architectures, YOLOv7 and Detection Transformer (DETR), across varying levels of data augmentation using CycleGAN. Our experiments focus on chest scan images within the context of biomedical informatics, specifically targeting the detection of abnormalities. The study reveals that YOLOv7 consistently outperforms DETR across all levels of augmented data, maintaining better performance even with 75% augmented data. Additionally, YOLOv7 demonstrates significantly faster convergence, requiring approximately 30 epochs compared to DETR's 300 epochs. These findings underscore the superiority of YOLOv7 for object detection tasks, especially in scenarios with limited data and when rapid convergence is essential. Our results provide valuable insights for researchers and practitioners in the field of computer vision, highlighting the effectiveness of YOLOv7 and the importance of data augmentation in improving model performance and efficiency.

목차

Abstract
1. Introduction
2. Related Work
2.1 You Only Look Once (YOLO)
2.2 Detection Transformer (DETR)
2.3 CycleGAN
2.4 Dataset: VinDr-CSR
3. Experiment Setup and Methodology
3.1 Setup Environment
3.2 Data Augmentation Scenario
3.3 Chest Abnormalities Detection
4. Result and Discussion
5. Conclusion
Acknowledgement
References

키워드

저자

• Yoshua Kaleb Purwanto [ Master Student, Department of Computer Engineering, Dongseo University, Busan, Korea ]
• Suk-Ho Lee [ Professor, Department of Computer Engineering, Dongseo University, Busan, Korea ]
• Dae-Ki Kang [ Professor, Department of Computer Engineering, Dongseo University, Busan, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380