강건한 CNN기반 수중 물체 인식을 위한 이미지 합성과 자동화된 Annotation ToolSynthesizing Image and Automated Annotation Tool for CNN based Under Water Object Detection

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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.
Publisher
한국로봇학회
Issue Date
2019-05
Language
Korean
Citation

로봇학회 논문지, v.14, no.2, pp.139 - 149

ISSN
1975-6291
DOI
10.7746/jkros.2019.14.2.139
URI
http://hdl.handle.net/10203/269949
Appears in Collection
CE-Journal Papers(저널논문)
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