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학술지 ETLi: Efficiently Annotated Traffic LiDAR Dataset using Incremental and Suggestive Annotation
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저자
강정규, 한승준, 김나현, 민경욱
발행일
202108
출처
ETRI Journal, v.43 no.4, pp.630-639
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.2021-0055
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.
키워드
Autonomous driving, deep-learning dataset, light detection and ranging, semantic segmentation, semantic simultaneous localization and mapping
KSP 제안 키워드
High Reliability, Image datasets, Learning Evaluation, Light conditions, Light detection and Ranging(LiDAR), Real-Time, Semantic segmentation, Two-dimensional image, autonomous driving, deep learning(DL), machine Learning
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