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Detail
Journal Article
ETLi: Efficiently Annotated Traffic LiDAR Dataset using Incremental and Suggestive Annotation
- Authors
- Jungyu Kang, Seung-Jun Han, Nahyeon Kim, Kyoung-Wook Min
- Issue Date
- 2021-08
- Citation
- ETRI Journal, v.43, no.4, pp.630-639
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- High Reliability, LIght Detection And Ranging(LIDAR), Learning evaluation, Light conditions, Real-time, Two-dimensional image, autonomous driving, image datasets, machine Learning, semantic information, simultaneous localization and mapping
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
