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Journal Article 모바일/임베디드 객체 및 장면 인식 기술 동향
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Authors
이수웅, 이근동, 고종국, 이승재, 유원영
Issue Date
2019-12
Citation
전자통신동향분석, v.34, no.6, pp.133-144
ISSN
1225-6455
Publisher
한국전자통신연구원
Language
Korean
Type
Journal Article
DOI
https://dx.doi.org/10.22648/ETRI.2019.J.340612
Abstract
Although deep learning-based visual image recognition technology has evolved rapidly, most of the commonly used methods focus solely on recognition accuracy. However, the demand for low latency and low power consuming image recognition with an acceptable accuracy is rising for practical applications in edge devices. For example, most Internet of Things (IoT) devices have a low computing power requiring more pragmatic use of these technologies; in addition, drones or smartphones have limited battery capacity again requiring practical applications that take this into consideration. Furthermore, some people do not prefer that central servers process their private images, as is required by high performance serverbased recognition technologies. To address these demands, the object and scene recognition technologies for mobile/embedded devices that enable optimized neural networks to operate in mobile and embedded environments are gaining attention. In this report, we briefly summarize the recent trends and issues of object and scene recognition technologies for mobile and embedded devices.
KSP Keywords
Battery capacity, Computing power, Edge devices, High performance, Image recognition technology, Internet of thing(IoT), Learning-based, Low latency, Low-Power, Mobile and embedded devices, Recent Trends
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
Type 4: