ETLi: Efficiently annotated traffic LiDAR dataset using incremental and suggestive annotation. Issue 4 (24th September 2021)

• Record Type: Journal Article
• Authors: Kang, Jungyu; Han, Seung‐Jun; Kim, Nahyeon; Min, Kyoung‐Wook
• Is Part Of: ETRI journal. Volume 43:Issue 4(2021)
• Page Start: 630
• Page End: 639
• Publication Date: 2021-09-24
• Subjects: Autonomous driving -- deep‐learning dataset -- light detection and ranging -- semantic segmentation -- semantic simultaneous localization and mapping; Telecommunication -- Periodicals; Electronics -- Periodicals; Electronics; Telecommunication; Periodicals; Periodicals; 621.38205
• DOI: 10.4218/etrij.2021-0055
• Languages: English
• ISSNs: 1225-6463

Abstract

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.