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ETLi: Efficiently annotated traffic LiDAR dataset using incremental and suggestive annotation
- Source :
- ETRI Journal, Vol 43, Iss 4, Pp 630-639 (2021)
- Publication Year :
- 2021
- Publisher :
- Electronics and Telecommunications Research Institute (ETRI), 2021.
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 12256463
- Volume :
- 43
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- ETRI Journal
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.7394e240b394d66a7dc1eeaf2770be4
- Document Type :
- article
- Full Text :
- https://doi.org/10.4218/etrij.2021-0055