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Detail
Journal Article
딥러닝 기반 객체 인식 기술 동향
- Issue Date
- 2018-08
- Citation
- 전자통신동향분석, v.33, no.4, pp.23-32
- ISSN
- 1225-6455
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- Korean
- Type
- Journal Article
- Abstract
- Object detection is a challenging field in the visual understanding research area, detecting objects in visual scenes, and the location of such objects. It has recently been applied in various fields such as autonomous driving, image surveillance, and face recognition. In traditional methods of object detection, handcrafted features have been designed for overcoming various visual environments; however, they have a trade-off issue between accuracy and computational efficiency. Deep learning is a revolutionary paradigm in the machine-learning field. In addition, because deep-learning-based methods, particularly convolutional neural networks (CNNs), have outperformed conventional methods in terms of object detection, they have been studied in recent years. In this article, we provide a brief descriptive summary of several recent deep-learning methods for object detection and deep learning architectures. We also compare the performance of these methods and present a research guide of the object detection field.
- KSP Keywords
- Computational Efficiency, Conventional methods, Convolution neural network(CNN), Deep Learning Architectures, Hand-crafted feature, Learning methods, Object detection, Trade-off, Traditional methods, Visual scenes, autonomous driving
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
