Earticle

초록

영어

Along with the current rapid development of technology, object classification is being researched, developed, and applied to security systems, autonomous driving, and other applications. A common technique is to use vision cameras to collect data of objects in the surrounding environment. Along with many other methods, LiDAR sensors are being used to collect data in space to detect and classify objects. By using the LiDAR sensors, some disadvantages of image sensors with the negative influence on the image quality by weather and light condition will be covered. In this study, a volumetric image descriptor in 3D shape is developed to handle 3D object data in the urban environment obtained from LiDAR sensors, and convert it into image data before using deep learning algorithms in the process of object classification. The study showed the potential possibility of the proposal and its further application.

목차

ABSTRACT
1. 서론
2. 라이다 데이터
2.1 KITTI 데이터
2.2 사전 처리
3. 공간적 이미지
3.1 3차원 큐브의 개념
3.2 이미지 생성
3.3 단일 채널 이미지
3.4 3채녈 이미지
4. 실험 결과
4.1 CNN 모델
4.2 데이터 준비
4.3 분류 결과
5. 결론
References

키워드

저자

• Huu Thu Nguyen [ Kongju National University ]
• 이세진 [ Sejin Lee | 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