- 초록
- 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.
- 저자
- 이수웅콘텐츠인식연구실suwoong@etri.re.kr
이근동콘텐츠인식연구실zacurr@etri.re.kr
고종국콘텐츠인식연구실jgko@etri.re.kr
이승재콘텐츠인식연구실seungjlee@etri.re.kr
유원영콘텐츠인식연구실zero2@etri.re.kr
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