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A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods

Kayal Lakshmanan, Matt Roach Orcid Logo, Cinzia Giannetti Orcid Logo, Shubham Bhoite, David George Orcid Logo, Tim Mortensen, Manduhu Manduhu, Behzad Heravi, Sharadha Kariyawasam, Xianghua Xie Orcid Logo

AI, Volume: 4, Issue: 2, Pages: 461 - 481

Swansea University Authors: Kayal Lakshmanan, Matt Roach Orcid Logo, Cinzia Giannetti Orcid Logo, Shubham Bhoite, Tim Mortensen, Xianghua Xie Orcid Logo

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DOI (Published version): 10.3390/ai4020025

Abstract

Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world m...

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Published in: AI
ISSN: 2673-2688
Published: MDPI AG 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63515
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spelling v2 63515 2023-05-22 A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods 31fdeba4e76994bc72c5b8954389f8ab Kayal Lakshmanan Kayal Lakshmanan true false 9722c301d5bbdc96e967cdc629290fec 0000-0002-1486-5537 Matt Roach Matt Roach true false a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 653a4e8cf7f72872e3670439b67d4393 Shubham Bhoite Shubham Bhoite true false 3356e4704f1c2da3270d82f758c595cb Tim Mortensen Tim Mortensen true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2023-05-22 ACEM Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world model for vehicle detection and classification from single-beam LiDAR of a roadside parking scenario. The paper presents a synthetically augmented transfer learning approach for LiDAR-based vehicle detection and the implementation of synthetic LiDAR data. A synthetic augmented transfer learning method was used to supplement the small real-world data set and allow the development of data-handling techniques. In addition, adding the synthetically augmented transfer learning method increases the robustness and overall accuracy of the model. Experiments show that the method can be used for fast deployment of the model for vehicle detection using a LIDAR sensor. Journal Article AI 4 2 461 481 MDPI AG 2673-2688 transfer learning; vehicle detection; LiDAR sensor; faster-RCNN; synthetic LiDAR data generation 1 6 2023 2023-06-01 10.3390/ai4020025 http://dx.doi.org/10.3390/ai4020025 AI Systems: Theory and Applications COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) EP/V061798/1 2024-06-27T13:32:49.5940759 2023-05-22T10:13:28.6052264 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering Kayal Lakshmanan 1 Matt Roach 0000-0002-1486-5537 2 Cinzia Giannetti 0000-0003-0339-5872 3 Shubham Bhoite 4 David George 0000-0001-7536-0797 5 Tim Mortensen 6 Manduhu Manduhu 7 Behzad Heravi 8 Sharadha Kariyawasam 9 Xianghua Xie 0000-0002-2701-8660 10 63515__27665__0ac8e5dd1e314ff59ad917c36345dfa2.pdf 63515.pdf 2023-06-01T11:04:21.1450565 Output 20473965 application/pdf Version of Record true © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). true eng https://creativecommons.org/licenses/by/4.0/
title A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods
spellingShingle A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods
Kayal Lakshmanan
Matt Roach
Cinzia Giannetti
Shubham Bhoite
Tim Mortensen
Xianghua Xie
title_short A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods
title_full A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods
title_fullStr A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods
title_full_unstemmed A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods
title_sort A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods
author_id_str_mv 31fdeba4e76994bc72c5b8954389f8ab
9722c301d5bbdc96e967cdc629290fec
a8d947a38cb58a8d2dfe6f50cb7eb1c6
653a4e8cf7f72872e3670439b67d4393
3356e4704f1c2da3270d82f758c595cb
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv 31fdeba4e76994bc72c5b8954389f8ab_***_Kayal Lakshmanan
9722c301d5bbdc96e967cdc629290fec_***_Matt Roach
a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia Giannetti
653a4e8cf7f72872e3670439b67d4393_***_Shubham Bhoite
3356e4704f1c2da3270d82f758c595cb_***_Tim Mortensen
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Kayal Lakshmanan
Matt Roach
Cinzia Giannetti
Shubham Bhoite
Tim Mortensen
Xianghua Xie
author2 Kayal Lakshmanan
Matt Roach
Cinzia Giannetti
Shubham Bhoite
David George
Tim Mortensen
Manduhu Manduhu
Behzad Heravi
Sharadha Kariyawasam
Xianghua Xie
format Journal article
container_title AI
container_volume 4
container_issue 2
container_start_page 461
publishDate 2023
institution Swansea University
issn 2673-2688
doi_str_mv 10.3390/ai4020025
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering
url http://dx.doi.org/10.3390/ai4020025
document_store_str 1
active_str 0
description Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world model for vehicle detection and classification from single-beam LiDAR of a roadside parking scenario. The paper presents a synthetically augmented transfer learning approach for LiDAR-based vehicle detection and the implementation of synthetic LiDAR data. A synthetic augmented transfer learning method was used to supplement the small real-world data set and allow the development of data-handling techniques. In addition, adding the synthetically augmented transfer learning method increases the robustness and overall accuracy of the model. Experiments show that the method can be used for fast deployment of the model for vehicle detection using a LIDAR sensor.
published_date 2023-06-01T13:32:49Z
_version_ 1803017592211767296
score 11.013731

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