Version 1
: Received: 25 February 2023 / Approved: 27 February 2023 / Online: 27 February 2023 (04:12:52 CET)
How to cite:
Gutierrez-Cabello, G.S.; Talavera, E.; Clavijo, M.; Iglesias, G.; Jimenez, F. A Novel Method to Generate Auto-Labeled Datasets for 3D
Vehicle Identification Using a New Contrast Model. Preprints2023, 2023020438. https://doi.org/10.20944/preprints202302.0438.v1.
Gutierrez-Cabello, G.S.; Talavera, E.; Clavijo, M.; Iglesias, G.; Jimenez, F. A Novel Method to Generate Auto-Labeled Datasets for 3D
Vehicle Identification Using a New Contrast Model. Preprints 2023, 2023020438. https://doi.org/10.20944/preprints202302.0438.v1.
Cite as:
Gutierrez-Cabello, G.S.; Talavera, E.; Clavijo, M.; Iglesias, G.; Jimenez, F. A Novel Method to Generate Auto-Labeled Datasets for 3D
Vehicle Identification Using a New Contrast Model. Preprints2023, 2023020438. https://doi.org/10.20944/preprints202302.0438.v1.
Gutierrez-Cabello, G.S.; Talavera, E.; Clavijo, M.; Iglesias, G.; Jimenez, F. A Novel Method to Generate Auto-Labeled Datasets for 3D
Vehicle Identification Using a New Contrast Model. Preprints 2023, 2023020438. https://doi.org/10.20944/preprints202302.0438.v1.
Abstract
Auto-labeling is one of the main challenges in 3D vehicle detection. Auto-labeled datasets can be used to identify objects in LiDAR data, which is a challenging task due to the large size of the dataset. In this work, we propose a novel methodology to generate new 3D based auto-labeling datasets with a different point of view setup than the one used in the most recognized datasets (KITTI, WAYMO, etc.). The performance of the methodology has been further demonstrated with the development of our own dataset with the auto-generated labels and tested under boundary conditions on a bridge in a fixed position. The proposed methodology is based on the YOLO model trained with the KITTI dataset. From a camera-LiDAR sensory fusion, it is intended to auto-label new datasets while maintaining the consistency of the Ground Truth. The main contribution of this work is a novel methodology to auto-label autonomous driving datasets using YOLO as the main labelling system. The performance of this approach is measured retraining the contrast models of the KITTI benchmark.
Keywords
Auto-labeled; LiDAR, Point of View, Deep Learning
Subject
ENGINEERING, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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