Multimodal geometric AutoEncoder (MGAE) for rail fasteners tightness evaluation with point clouds & monocular depth fusion
•Introduces a Multimodal Geometric AutoEncoder (MGAE) for rail fastener tightness evaluation.•Integrates point cloud and monocular depth fusion for improved feature extraction.•Reduces manual annotation efforts and increases computational efficiency in rail inspection.•Provides a scalable, automated...
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| Veröffentlicht in: | Measurement : journal of the International Measurement Confederation Jg. 244; S. 116557 |
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| Sprache: | Englisch |
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28.02.2025
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| ISSN: | 0263-2241 |
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| Abstract | •Introduces a Multimodal Geometric AutoEncoder (MGAE) for rail fastener tightness evaluation.•Integrates point cloud and monocular depth fusion for improved feature extraction.•Reduces manual annotation efforts and increases computational efficiency in rail inspection.•Provides a scalable, automated solution for rail infrastructure maintenance.
Accurate detection and estimation of railway fastener tightness are vital for rail infrastructure safety and reliability. Traditional methods depend on manual annotation tools like Label Me, which are error-prone, labor-intensive, and costly. Additionally, monocular depth estimation and instance segmentation involve complex computations that challenge real-time implementation, particularly on resource-constrained platforms. This study introduces a novel three-phase solution using the Multimodal Geometric Autoencoder (MGAE) for fastener tightness detection, integrating point clouds with monocular-depth-guided multimodal data. Our approach utilizes a hybrid autoencoder for high-quality feature extraction, enabling precise tightness estimation. Employing unsupervised learning, MGAE eliminates the need for labeled data, thus reducing labor and costs. The framework integrates point clouds, mesh, monocular depth, and 2D images, with various fusion blocks enhancing feature extraction accuracy and computational efficiency. Post-feature extraction, classical techniques such as isolation forest, stress–strain, and elastic potential energy methods assess fastener tightness. |
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| AbstractList | •Introduces a Multimodal Geometric AutoEncoder (MGAE) for rail fastener tightness evaluation.•Integrates point cloud and monocular depth fusion for improved feature extraction.•Reduces manual annotation efforts and increases computational efficiency in rail inspection.•Provides a scalable, automated solution for rail infrastructure maintenance.
Accurate detection and estimation of railway fastener tightness are vital for rail infrastructure safety and reliability. Traditional methods depend on manual annotation tools like Label Me, which are error-prone, labor-intensive, and costly. Additionally, monocular depth estimation and instance segmentation involve complex computations that challenge real-time implementation, particularly on resource-constrained platforms. This study introduces a novel three-phase solution using the Multimodal Geometric Autoencoder (MGAE) for fastener tightness detection, integrating point clouds with monocular-depth-guided multimodal data. Our approach utilizes a hybrid autoencoder for high-quality feature extraction, enabling precise tightness estimation. Employing unsupervised learning, MGAE eliminates the need for labeled data, thus reducing labor and costs. The framework integrates point clouds, mesh, monocular depth, and 2D images, with various fusion blocks enhancing feature extraction accuracy and computational efficiency. Post-feature extraction, classical techniques such as isolation forest, stress–strain, and elastic potential energy methods assess fastener tightness. |
| ArticleNumber | 116557 |
| Author | Ehsan, Haleema Atta, Zunaira Qiu, Shi Muhammad Ahmed Hassan Shah, S. Peng, Jun Faizan Hussain Shah, Syed Wang, Weidong Wang, Jin Zaheer, Qasim Ai, Chengbo |
| Author_xml | – sequence: 1 givenname: Shi surname: Qiu fullname: Qiu, Shi organization: School of Civil Engineering, Central South University, Changsha 410075, China – sequence: 2 givenname: Qasim surname: Zaheer fullname: Zaheer, Qasim organization: School of Civil Engineering, Central South University, Changsha 410075, China – sequence: 3 givenname: S. surname: Muhammad Ahmed Hassan Shah fullname: Muhammad Ahmed Hassan Shah, S. organization: School of Civil Engineering, Central South University, Changsha 410075, China – sequence: 4 givenname: Syed surname: Faizan Hussain Shah fullname: Faizan Hussain Shah, Syed organization: School of Civil Engineering, Central South University, Changsha 410075, China – sequence: 5 givenname: Haleema surname: Ehsan fullname: Ehsan, Haleema organization: School of Civil Engineering, Central South University, Changsha 410075, China – sequence: 6 givenname: Zunaira surname: Atta fullname: Atta, Zunaira organization: School of Civil Engineering, Central South University, Changsha 410075, China – sequence: 7 givenname: Chengbo surname: Ai fullname: Ai, Chengbo organization: Department of Civil and Environmental Engineering, University of Massachusetts, Amherst, MA 01003, USA – sequence: 8 givenname: Jin surname: Wang fullname: Wang, Jin organization: School of Civil Engineering, Central South University, Changsha 410075, China – sequence: 9 givenname: Weidong surname: Wang fullname: Wang, Weidong organization: School of Civil Engineering, Central South University, Changsha 410075, China – sequence: 10 givenname: Jun orcidid: 0000-0002-8027-3396 surname: Peng fullname: Peng, Jun email: civilpengjun@csu.edu.cn organization: School of Civil Engineering, Central South University, Changsha 410075, China |
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| Cites_doi | 10.1109/ACCESS.2017.2685629 10.1109/IAEAC54830.2022.9929911 10.1109/TITS.2023.3319135 10.1016/j.actaastro.2023.08.001 10.1016/j.trc.2017.04.011 10.1007/s13042-013-0223-z 10.1016/j.trc.2018.05.007 10.1109/ACCESS.2024.3388889 10.1108/EC-01-2022-0034 10.1007/s00202-022-01590-9 10.1016/j.trc.2018.02.019 10.3390/ai5030064 10.3390/s110807364 10.1111/mice.13173 10.1016/j.engappai.2019.01.008 10.1109/TIE.2020.3013748 10.1016/j.trc.2019.02.001 10.3390/s20051367 10.1109/TAES.2024.3404915 10.3390/s22176409 10.1109/ACCESS.2021.3053408 10.1007/s44163-024-00127-2 10.1109/TCSVT.2021.3049869 10.1109/TCSVT.2017.2740321 10.1016/j.asr.2024.06.002 10.1109/TPAMI.2020.2983686 10.1109/ACCESS.2019.2961686 10.1109/TITS.2021.3095167 10.3233/IFS-151883 10.1109/TSMC.2024.3373031 10.1109/TPAMI.2017.2699184 10.1016/j.trc.2022.103679 10.1016/j.ress.2012.03.017 10.1016/j.actaastro.2024.06.002 10.1007/978-981-99-5804-7_6 10.1108/TQM-10-2023-0347 10.1109/ICCV.2015.304 10.1109/JIOT.2021.3126875 10.3390/app13126982 10.1007/s11071-024-10291-w 10.1109/TIM.2013.2283741 10.1016/j.autcon.2016.06.008 10.1016/j.trc.2018.04.001 |
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| Keywords | Rail infrastructure Multimodal approach Autoencoder Rail fasteners Fastener tightness |
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| SubjectTerms | Autoencoder Fastener tightness Multimodal approach Rail fasteners Rail infrastructure |
| Title | Multimodal geometric AutoEncoder (MGAE) for rail fasteners tightness evaluation with point clouds & monocular depth fusion |
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