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.

Abstract
 1. 서 론
 2. 제조 공정에서의 물체 인식 및 자세 추정
  2.1 물체 인식 및 자세 추정 문제의 특성
  2.2 실시간 물체 검출 및 자세 추정
 3. 딥러닝 기반 제조 공정 물체 인식 및 자세추정 기법
  3.1 물체 인식을 위한 YOLO 모듈
  3.2 최적의 외곽선 검출
  3.3 물체 자세 추정
 4. 실험 및 결과
 5. 결 론
 References

• 조재민(Computer Software, Korea University of Science and Technology) | Jaemin Cho
• 강상승(Electronics and Telecommunications Research Institute, Daejeon) | Sang Seung Kang
• 김계경(Electronics and Telecommunications Research Institute, Daejeon) | Kye Kyung Kim Corresponding author