Earticle

초록

영어

Object detection is an important field of computer vision and is applied to applications such as security, autonomous driving, and face recognition. Recently, as the application of artificial intelligence technology including deep learning has been applied in various fields, it has become a more powerful tool that can learn meaningful high-level, deeper features, solving difficult problems that have not been solved. Therefore, deep learning techniques are also being studied in the field of object detection, and algorithms with excellent performance are being introduced. In this paper, a deep learning-based object detection algorithm used to detect multiple objects in an image is investigated, and future development directions are presented.

목차

Abstract
1. INTRODUCTION
2. OBJECT DETECTION METHOD
2.1 R-CNN
2.2 Fast R-CNN
2.3 Faster R-CNN
2.4 Mask R-CNN
2.5 YOLO
2.6 SSD
2.7 RefineDet
2.8 Relation Networks for Object Detection
2.9 CenterNet
2.10 EfficientDet
3. Research direction of object detection
3.1 Combined one-stage detector and two-stage detector in object detection networks
3.2 Efficient post-processing method
3.3 Multi-domain object detection
3.4 Unsupervised object detection
3.5 Multi-source information support
3.6 Object detection system for portable terminals
3.7 GAN-based object detection system
4. Conclusion
5. ACKNOWLEDGEMENT
REFERENCES

키워드

저자

• NaeJoung Kwak [ Prof. Dept. of Cyber and Security , Baejae Univ., Korea ]
• DongJu Kim [ Prof. Postech Institute of Artificial Intelligence, POSTECH, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700