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A Robust Vehicle Detection Model for LiDAR Sensor Using Simulation Data and Transfer Learning Methods
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Cinzia Giannetti ,
Shubham Bhoite,
David George ,
Tim Mortensen,
Manduhu Manduhu,
Behzad Heravi,
Sharadha Kariyawasam,
Xianghua Xie
AI, Volume: 4, Issue: 2, Pages: 461 - 481
Swansea University Authors:
Kayal Lakshmanan, Matt Roach , Cinzia Giannetti , Shubham Bhoite, Tim Mortensen, Xianghua Xie
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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...
| Published in: | AI |
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| ISSN: | 2673-2688 |
| Published: |
MDPI AG
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa63515 |
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| 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 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. |
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| Item Description: |
AI Systems: Theory and Applications |
| Keywords: |
transfer learning; vehicle detection; LiDAR sensor; faster-RCNN; synthetic LiDAR data generation |
| College: |
Faculty of Science and Engineering |
| Funders: |
EP/V061798/1 |
| Issue: |
2 |
| Start Page: |
461 |
| End Page: |
481 |