Automatic detection and identification of object by fusing camera and marine radar measurements = 카메라와 해상 레이더 측정값을 융합한 객체의 자동 탐지 및 식별

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As research on unmanned systems has been actively conducted recently, technology that recognizes obstacles and the surrounding environment, which is required for performing various tasks effectively, has become important. In maritime environments, radar has been used as a primary sensor to detect objects for navigation and collision avoidance, but recently, cameras are also being considered to improve the reliability and performance of detection and to perform it automatically. This study addresses active detection and identification by matching the relative position of floating objects detected by radar images and ships detected in camera images. First, convolutional neural networks are used to detect ships from camera images and to semantically classify marine radar images into floating object, noise, and land. Then, a newly developed robust data association algorithm is applied, using parameters representing the correlation between two sensor measurements. The performance of the proposed algorithm is validated using a camera and radar dataset obtained in real maritime environments.
Advisors
Kim, Jinwhanresearcher김진환researcher
Description
한국과학기술원 :기계공학과,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 기계공학과, 2020.2,[iv, 36 p. :]

Keywords

Data association▼aConvolutional neural network▼aObject detection▼aSemantic segmentation▼aMarine radar▼aMonocular camera; 데이터 연관▼a합성곱 신경망▼a객체 탐지▼a의미론적 분할▼a해상 레이더▼a단안 카메라

URI
http://hdl.handle.net/10203/284076
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=910889&flag=dissertation
Appears in Collection
ME-Theses_Master(석사논문)
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