A robot usually adopts ANN (artificial neural network)-based object detection and instance segmentation algorithms to recognize objects but creating datasets for these algorithms requires high labeling costs because the dataset should be manually labeled. In order to lower the labeling cost, a new scheme is proposed that can automatically generate a training images and label them for specific objects. This scheme uses an instance segmentation algorithm trained to give the masks of unknown objects, so that they can be obtained in a simple environment. The RGB images of objects can be obtained by using these masks, and it is necessary to label the classes of objects through a human supervision. After obtaining object images, they are synthesized with various background images to create new images. Labeling the synthesized images is performed automatically using the masks and previously input object classes. In addition, human intervention is further reduced by using the robot arm to collect object images. The experiments show that the performance of instance segmentation trained through the proposed method is equivalent to that of the real dataset and that the time required to generate the dataset can be significantly reduced.

Abstract
 1. 서 론
 2. 데이터셋 생성 방법
  2.1 물체 이미지 수집
  2.2 배경 제거
  2.3 이미지 합성
  2.4 자동 레이블링
 3. 배경 제거를 위한 Mask R-CNN의 학습
  3.1 Mask R-CNN
  3.2 배경 제거를 위한 데이터셋 구조
  3.3 학습 세부 사항
 4. 실험 결과
  4.1 실험 환경 구성
  4.2 실험 결과
  4.3 실험 결과 해석
 5. 결 론
 References

• 조현준(Korea University) | HyunJun Jo
• 김다윗(Korea University) | Dawit Kim
• 송재복(Mechanical Engineering, Korea University) | Jae-Bok Song Corresponding author