로봇학회논문지 (The Journal of Korea Robotics Society)

  • 제14권1호
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  • Pages.14-21
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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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수중 소나 영상 학습 데이터의 왜곡 및 회전 Augmentation을 통한 딥러닝 기반의 마커 검출 성능에 관한 연구

Study of Marker Detection Performance on Deep Learning via Distortion and Rotation Augmentation of Training Data on Underwater Sonar Image

  • Lee, Eon-Ho (Mechanical Engineering, Kongju National University) ;
  • Lee, Yeongjun (Korea Research Institute of Ships and Ocean Engineering) ;
  • Choi, Jinwoo (Korea Research Institute of Ships and Ocean Engineering) ;
  • Lee, Sejin (Division of Mechanical & Automotive Engineering, Kongju National University)
  • 투고 : 2018.12.07
  • 심사 : 2019.01.10
  • 발행 : 2019.02.28
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초록

In the ground environment, mobile robot research uses sensors such as GPS and optical cameras to localize surrounding landmarks and to estimate the position of the robot. However, an underwater environment restricts the use of sensors such as optical cameras and GPS. Also, unlike the ground environment, it is difficult to make a continuous observation of landmarks for location estimation. So, in underwater research, artificial markers are installed to generate a strong and lasting landmark. When artificial markers are acquired with an underwater sonar sensor, different types of noise are caused in the underwater sonar image. This noise is one of the factors that reduces object detection performance. This paper aims to improve object detection performance through distortion and rotation augmentation of training data. Object detection is detected using a Faster R-CNN.

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참고문헌

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피인용 문헌

  1. 실시간 순환 신경망 기반의 멀티빔 소나 이미지를 이용한 수중 물체의 추적에 관한 연구 vol.15, pp.1, 2020, https://doi.org/10.7746/jkros.2020.15.1.008
  2. Marker-Based Method for Recognition of Camera Position for Mobile Robots vol.21, pp.4, 2019, https://doi.org/10.3390/s21041077