대한임베디드공학회논문지 (IEMEK Journal of Embedded Systems and Applications)

대한임베디드공학회 (Institute of Embedded Engineering of Korea)
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간병 로봇을 위한 합성곱 신경망 (CNN) 기반 의약품 인식기 설계

Design of Convolution Neural Network (CNN) Based Medicine Classifier for Nursing Robots

  • 투고 : 2021.08.26
  • 심사 : 2021.10.06
  • 발행 : 2021.10.31
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초록

Our final goal is to implement nursing robots that can recognize patient's faces and their medicine on prescription. They can help patients to take medicine on time and prevent its abuse for recovering their health soon. As the first step, we proposed a medicine classifier with a low computational network that is able to run on embedded PCs without GPU in order to be applied to universal nursing robots. We confirm that our proposed model called MedicineNet achieves an 99.99% accuracy performance for classifying 15 kinds of medicines and background images. Moreover, we realize that the calculation time of our MedicineNet is about 8 times faster than EfficientNet-B0 which is well known as ImageNet classification with the high performance and the best computational efficiency.

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과제정보

본 논문은 과학기술정보통신부 정보통신창의인재양성사업의 지원을 통해 수행한 ICT멘토링 프로젝트 결과물임.

참고문헌

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