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딥러닝 기반의 도심 물체 분류를 위한 공간적 라이다 데이터 특징 표현기의 개발

원문정보

Development of Volumetric LiDAR Data Feature Descriptor for Urban Object Classification Based on Deep Learning

Huu Thu Nguyen, 이세진

피인용수 : 0(자료제공 : 네이버학술정보)

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

저자정보

  • Huu Thu Nguyen Kongju National University
  • 이세진 Sejin Lee. Kongju National University

참고문헌

자료제공 : 네이버학술정보

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