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구분 SCI
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학술지 비주얼 서보잉을 위한 딥러닝 기반 물체 인식 및 자세 추정
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저자
조재민, 강상승, 김계경
발행일
201903
출처
로봇학회논문지, v.14 no.1, pp.1-7
ISSN
1975-6291
출판사
한국로봇학회
DOI
https://dx.doi.org/10.7746/jkros.2019.14.1.001
협약과제
18PS1500, 작업자 공간공유 및 스마트공장 적용을 위한 차세대 제조용 로봇, 강상승
초록
Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.
KSP 제안 키워드
Automation technologies, Bin-picking, Fast R-CNN, Industrial Revolution, Manual work, Real-time application, Small objects, Smart Factory, Vision sensor, deep learning(DL), deep learning algorithm