로봇학회논문지 (The Journal of Korea Robotics Society)

  • 제14권2호
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  • Pages.139-149
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  • 2019
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  • 1975-6291(pISSN)
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  • 2287-3961(eISSN)
한국로봇학회 (Korea Robotics Society)
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강건한 CNN기반 수중 물체 인식을 위한 이미지 합성과 자동화된 Annotation Tool

Synthesizing Image and Automated Annotation Tool for CNN based Under Water Object Detection

  • 투고 : 2019.01.08
  • 심사 : 2019.03.14
  • 발행 : 2019.05.31
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초록

In this paper, we present auto-annotation tool and synthetic dataset using 3D CAD model for deep learning based object detection. To be used as training data for deep learning methods, class, segmentation, bounding-box, contour, and pose annotations of the object are needed. We propose an automated annotation tool and synthetic image generation. Our resulting synthetic dataset reflects occlusion between objects and applicable for both underwater and in-air environments. To verify our synthetic dataset, we use MASK R-CNN as a state-of-the-art method among object detection model using deep learning. For experiment, we make the experimental environment reflecting the actual underwater environment. We show that object detection model trained via our dataset show significantly accurate results and robustness for the underwater environment. Lastly, we verify that our synthetic dataset is suitable for deep learning model for the underwater environments.

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참고문헌

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피인용 문헌

  1. 수중에서의 특징점 매칭을 위한 CNN기반 Opti-Acoustic변환 vol.15, pp.1, 2020, https://doi.org/10.7746/jkros.2020.15.1.001