Earticle

초록

영어

This paper presents the novel observation model, called Modified Spherical Signature Descriptor(MSSD), capable of representing 2D image generated from 3D point cloud data. The Modified Spherical Signature Descriptor has a uniform mesh grid to accumulate the occupancy evidence caused by neighbor point cloud data. According to a kind of area such as wall, road, tree, car, and so on, the evidence pattern of 2D image looks so different each other. For the parameter learning of Convolutional Neural Network(CNN) layers, these 2D images were applied as the input layer. The Convolutional Neural Network, one of the deep learning methods and familiar with the image analysis, was utilized for the urban structure classification. The case study on CNN practice was introduced in detail in this paper. The simulation results shows that the classification accuracy of CNN with 2D images of the proposed MSSD was improved more than the traditional methods' one.

목차

ABSTRACT
 1. 서론
  1.1 연구 배경
  1.2 연구 동향
 2. 수정 구형 특징 표현기
  2.1 문제 정의
  2.2 격자 점유 확률 갱신
 3. 컨볼루션 신경망
 4. 실험
  4.1 실험 데이터
  4.2 모델 파라미터
  4.3 실험 결과
 5. 결론
 후기
 References

키워드

저자

• 이언호 [ Eon-Ho Lee | Department of Mechanical Engineering, Kongju National University ]
• 이세진 [ Se-Jin Lee | Member, Division of Mechanical and Automotive Engineering, Kongju National University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국기계항공기술학회지(구 한국기계기술학회지) [Journal of the Korean Society of Mechanical and Aviation Technology ]

• 간기

  격월간

• pISSN

  1229-604X

• eISSN

  2508-3805

• 수록기간

  1999~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 550 DDC 620