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.

Abstract
 1. 서 론
 2. 관련 연구
  2.1 수중 초음파센서
  2.2 물체 감지
 3. 데이터 Augmentation
  3.1 학습 데이터
  3.2 테스트 데이터
 4. 실험 결과
  4.1 Rotation Augmentation
  4.2 테스트 데이터의 세트화
 5. 결 론
 References

• 이언호(Mechanical Engineering, Kongju National University) | Eon-Ho Lee
• 이영준(Korea Research Institute of Ships and Ocean Engineering, Daejeon) | Yeongjun Lee
• 최진우(Korea Research Institute of Ships and Ocean Engineering, Daejeon) | Jinwoo Choi
• 이세진(Division of Mechanical & Automotive Engineering, Kongju National University) | Sejin Lee Corresponding author