| Autor: |
Jungyu Kang, Seung‐Jun Han, Nahyeon Kim, Kyoung‐Wook Min |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
ETRI Journal, Vol 43, Iss 4, Pp 630-639 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1225-6463 |
| DOI: |
10.4218/etrij.2021-0055 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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