A Specialized Database for Autonomous Vehicles Based on the KITTI Vision Benchmark
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 clou...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/14/3165 |
| _version_ | 1797589543158808576 |
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| author | Juan I. Ortega-Gomez Luis A. Morales-Hernandez Irving A. Cruz-Albarran |
| author_facet | Juan I. Ortega-Gomez Luis A. Morales-Hernandez Irving A. Cruz-Albarran |
| author_sort | Juan I. Ortega-Gomez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T01:07:58Z |
| format | Article |
| id | doaj.art-a4ac507625df4d43ad4217bf636313c6 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T01:07:58Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a4ac507625df4d43ad4217bf636313c62023-11-18T19:06:48ZengMDPI AGElectronics2079-92922023-07-011214316510.3390/electronics12143165A Specialized Database for Autonomous Vehicles Based on the KITTI Vision BenchmarkJuan I. Ortega-Gomez0Luis A. Morales-Hernandez1Irving A. Cruz-Albarran2Faculty of Engineering, San Juan del Río Campus, Autonomous University of Querétaro, San Juan del Río 76807, Querétaro, MexicoFaculty of Engineering, San Juan del Río Campus, Autonomous University of Querétaro, San Juan del Río 76807, Querétaro, MexicoFaculty of Engineering, San Juan del Río Campus, Autonomous University of Querétaro, San Juan del Río 76807, Querétaro, MexicoAutonomous 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.https://www.mdpi.com/2079-9292/12/14/3165autonomous drivingdriverless vehicleenvironment perceptionLiDARpoint cloudtop view |
| spellingShingle | Juan I. Ortega-Gomez Luis A. Morales-Hernandez Irving A. Cruz-Albarran A Specialized Database for Autonomous Vehicles Based on the KITTI Vision Benchmark Electronics autonomous driving driverless vehicle environment perception LiDAR point cloud top view |
| title | A Specialized Database for Autonomous Vehicles Based on the KITTI Vision Benchmark |
| title_full | A Specialized Database for Autonomous Vehicles Based on the KITTI Vision Benchmark |
| title_fullStr | A Specialized Database for Autonomous Vehicles Based on the KITTI Vision Benchmark |
| title_full_unstemmed | A Specialized Database for Autonomous Vehicles Based on the KITTI Vision Benchmark |
| title_short | A Specialized Database for Autonomous Vehicles Based on the KITTI Vision Benchmark |
| title_sort | specialized database for autonomous vehicles based on the kitti vision benchmark |
| topic | autonomous driving driverless vehicle environment perception LiDAR point cloud top view |
| url | https://www.mdpi.com/2079-9292/12/14/3165 |
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