• 한국해양공학회지
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• 제30권3호
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• pp.214-220
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• 2016

Imaging sonars such as side-scanning sonar or forward-looking sonar are becoming fundamental sensors in the underwater robotics field. However, using sonar images for underwater perception presents many challenges. Sonar images are usually low resolution with inherent speckled noise. To overcome the limited sensor information for underwater perception, we investigated preprocessing methods for sonar images and feature detection methods for a nonlinear scale space. In this paper, we focus on a comparative analysis of (1) preprocessing for sonar images and (2) the feature detection performance in relation to the scale space composition.

• Ocean Systems Engineering
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• 제10권4호
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• pp.435-449
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• 2020

This paper proposes a method to estimate the underwater target object's yaw angle using a sonar image. A simulator modeling imaging mechanism of a sonar sensor and a generative adversarial network for style transfer generates realistic template images of the target object by predicting shapes according to the viewing angles. Then, the target object's yaw angle can be estimated by comparing the template images and a shape taken in real sonar images. We verified the proposed method by conducting water tank experiments. The proposed method was also applied to AUV in field experiments. The proposed method, which provides bearing information between underwater objects and the sonar sensor, can be applied to algorithms such as underwater localization or multi-view-based underwater object recognition.

• 로봇학회논문지
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• 제15권1호
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• pp.1-7
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• 2020

In this paper, we introduce the methodology that utilizes deep learning-based front-end to enhance underwater feature matching. Both optical camera and sonar are widely applicable sensors in underwater research, however, each sensor has its own weaknesses, such as light condition and turbidity for the optic camera, and noise for sonar. To overcome the problems, we proposed the opti-acoustic transformation method. Since feature detection in sonar image is challenging, we converted the sonar image to an optic style image. Maintaining the main contents in the sonar image, CNN-based style transfer method changed the style of the image that facilitates feature detection. Finally, we verified our result using cosine similarity comparison and feature matching against the original optic image.

• 한국해양공학회지
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• 제30권3호
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• pp.227-233
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• 2016

This paper presents an underwater structure 3D reconstruction method using a 2D multibeam imaging sonar. Compared with other underwater environmental recognition sensors, the 2D multibeam imaging sonar offers high resolution images in water with a high turbidity level by showing the reflection intensity data in real-time. With such advantages, almost all underwater applications, including ROVs, have applied this 2D multibeam imaging sonar. However, the elevation data are missing in sonar images, which causes difficulties with correctly understanding the underwater topography. To solve this problem, this paper concentrates on the physical relationship between the sonar image and the scene topography to find the elevation information. First, the modeling of the sonar reflection intensity data is studied using the distances and angles of the sonar beams and underwater objects. Second, the elevation data are determined based on parameters like the reflection intensity and shadow length. Then, the elevation information is applied to the 3D underwater reconstruction. This paper evaluates the presented real-time 3D reconstruction method using real underwater environments. Experimental results are shown to appraise the performance of the method. Additionally, with the utilization of ROVs, the contour and texture image mapping results from the obtained 3D reconstruction results are presented as applications.

• Journal of Advanced Marine Engineering and Technology
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• 제33권2호
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• pp.344-354
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• 2009

Side scan sonar using acoustic wave plays a very important role in the underwater, sea floor, and shallow marine geologic survey. In this study, we have acquired side scan sonar data for the underwater man-made structures, artificial reefs and fishing grounds, installed and distributed in the survey area. We applied digital image processing techniques to side scan sonar data in order to improve and enhance an image quality. We carried out digital image processing with various kinds of filtering in spatial domain and frequency domain. We tested filtering parameters such as kernel size, differential operator, and statistical value. We could easily estimate the conditions, distribution and environment of artificial structures through the interpretation of side scan sonar.

• 전자공학회논문지
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• 제53권3호
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• pp.91-98
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• 2016

소나 영상에서 수중 물체의 검출과 분류는 도전적인 과제이다. 본 논문에서는 소나 영상과 영상처리기법을 이용하여 해저의 물체를 식별하는 시스템을 제안한다. 수중 물체의 식별 과정은 수중 물체 후보 영역 검출과 물체 식별의 두 단계로 구성된다. 영상 정합(image registration) 기법을 이용하여 수중 물체 후보 영역을 검출하고, 기존에 획득된 기준 배경 영상과 현재 스캔된 영상 사이의 공통된 특징점을 검출하여 정합한 후, 두 영상의 차 영상(difference image)을 구하여 검출한다. 검출된 물체는 고유벡터와 고유값을 특징으로 사용하여 데이터베이스내의 패턴과 가장 유사한 패턴으로 분류한다. 제안하는 수중 물체 식별 시스템은 최단 소행 항로(Q route) 확보와 같은 응용에 효율적으로 사용될 수 있을 것으로 기대된다.

• 로봇학회논문지
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• 제14권1호
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• pp.14-21
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• 2019

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.

• 한국음향학회지
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• 제37권2호
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• pp.118-128
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• 2018

본 논문은 사이드 스캔 소나 영상을 컨볼루션 신경망으로 학습하여 수중물체를 탐색하는 방법을 다루었다. 사이드 스캔 소나 영상을 사람이 직접 분석하던 방법에서 컨볼루션 신경망 알고리즘이 보강되면 분석의 효율성을 높일 수 있다. 연구에 사용한 사이드 스캔 소나의 영상 데이터는 미 해군 수상전센터에서 공개한 자료이고 4종류의 합성수중물체로 구성되었다. 컨볼루션 신경망 알고리즘은 관심영역 기반으로 학습하는 Faster R-CNN(Region based Convolutional Neural Networks)을 기본으로 하며 신경망의 세부사항을 보유한 데이터에 적합하도록 구성하였다. 연구의 결과를 정밀도-재현율 곡선으로 비교하였고 소나 영상 데이터에 지정한 관심영역의 변경이 탐지성능에 미치는 영향을 검토함으로써 컨볼루션 신경망의 수중물체 탐지 적용성에 대해 살펴보았다.

• 로봇학회논문지
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• 제9권4호
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• pp.232-241
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• 2014

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2005년도 후기학술대회논문집
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• pp.152-153
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• 2005

Side scan sonar System occupies an important position as one of marine survey equipments. The purpose of this research is to express sonar' scan images in underwater and compare with the measured size, shape and the quality of the material. Also we confirm the effectiveness of obtained images using the Side scan sonar.