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DC Field | Value | Language |
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dc.contributor.author | Marsh, B | - |
dc.contributor.author | Sadka, AH | - |
dc.contributor.author | Bahai, H | - |
dc.date.accessioned | 2022-12-05T12:08:21Z | - |
dc.date.available | 2022-12-05T12:08:21Z | - |
dc.date.issued | 2022-12-01 | - |
dc.identifier.citation | Marsh B. et. al.(2022) 'A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques', Sensors, 22 (23), 9364, pp.1-19. doi: 10.3390/s22239364. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/25585 | - |
dc.description.abstract | In this review, we provide a detailed coverage of multi-sensor fusion techniques that use RGB stereo images and a sparse LiDAR-projected depth map as input data to output a dense depth map prediction. We cover state-of-the-art fusion techniques which, in recent years, have been deep learning-based methods that are end-to-end trainable. We then conduct a comparative evaluation of the state-of-the-art techniques and provide a detailed analysis of their strengths and limitations as well as the applications they are best suited for. | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright: © 2022 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/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | sensor fusion | en_US |
dc.subject | stereo | en_US |
dc.subject | LiDAR | en_US |
dc.subject | deep learning | en_US |
dc.title | A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/s22239364 | - |
dc.relation.isPartOf | Sensors | - |
pubs.publication-status | Published | - |
dc.identifier.eissn | 1424-8220 | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Institute of Digital Futures Institute of Materials and Manufacturing |
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FullText.pdf | Copyright: © 2022 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/). | 9.79 MB | Adobe PDF | View/Open |
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