상세정보
학술지
ETLi: Efficiently Annotated Traffic LiDAR Dataset using Incremental and Suggestive Annotation
- 발행일
- 202108
- 출처
- ETRI Journal, v.43 no.4, pp.630-639
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 협약과제
- 21HS2600, 대중교통 소외지역 이동 및 생활안전 사회문제해결을 위한 표준플랫폼 기반 자율주행기술개발, 민경욱
- 초록
- Autonomous driving requires a computerized perception of the environment for safety and machine-learning evaluation. Recognizing semantic information is difficult, as the objective is to instantly recognize and distinguish items in the environment. Training a model with real-time semantic capability and high reliability requires extensive and specialized datasets. However, generalized datasets are unavailable and are typically difficult to construct for specific tasks. Hence, a light detection and ranging semantic dataset suitable for semantic simultaneous localization and mapping and specialized for autonomous driving is proposed. This dataset is provided in a form that can be easily used by users familiar with existing two-dimensional image datasets, and it contains various weather and light conditions collected from a complex and diverse practical setting. An incremental and suggestive annotation routine is proposed to improve annotation efficiency. A model is trained to simultaneously predict segmentation labels and suggest class-representative frames. Experimental results demonstrate that the proposed algorithm yields a more efficient dataset than uniformly sampled datasets.
- KSP 제안 키워드
- High Reliability, Image datasets, Learning Evaluation, Light conditions, Light detection and Ranging(LiDAR), Real-Time, Two-dimensional image, autonomous driving, machine Learning, semantic information, simultaneous localization and mapping
본 저작물은 공공누리 제4유형 : 출처표시 + 상업적 이용금지 + 변경금지 조건에 따라 이용할 수 있습니다.
