TY  - JOUR
PY  - 2023//
TI  - A specialized database for autonomous vehicles based on the KITTI vision benchmark
JO  - Electronics (Basel, Switzerland)
A1  - Ortega-Gomez, Juan I.
A1  - Morales-Hernandez, Luis A.
A1  - Cruz-Albarran, Irving A.
SP  - e3165
EP  - e3165
VL  - 12
IS  - 14
N2  - Autonomous driving systems have emerged with the promise of preventing accidents. The first critical aspect of these systems is perception, where the regular practice is the use of top-view point clouds as the input; however, the existing databases in this area only present scenes with 3D point clouds and their respective labels. This generates an opportunity, and the objective of this work is to present a database with scenes directly in the top-view and their labels in the respective plane, as well as adding a segmentation map for each scene as a label for segmentation work. The method used during the creation of the proposed database is presented; this covers how to transform 3D to 2D top-view image point clouds, how the detection labels in the plane are generated, and how to implement a neural network for the generated segmentation maps of each scene. Using this method, a database was developed with 7481 scenes, each with its corresponding top-view image, label file, and segmentation map, where the road segmentation metrics are as follows: F1, 95.77; AP, 92.54; ACC, 97.53; PRE, 94.34; and REC, 97.25. This article presents the development of a database for segmentation and detection assignments, highlighting its particular use for environmental perception works.<p />  <p>Language: en</p>
LA  - en
SN  - 2079-9292
UR  - http://dx.doi.org/10.3390/electronics12143165
ID  - ref1
ER  -