A visual measurement algorithm for vibration displacement of rotating body using semantic segmentation network
Compared with the traditional vibration displacement measurement methods, visual vibration measurement offers several advantages such as long-distance capability, non-contact operation and easy installation. However, the phenomenon of low fitting accuracy of the bounding box often occurs when detect...
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| Veröffentlicht in: | Expert systems with applications Jg. 237; S. 121306 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
01.03.2024
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | Compared with the traditional vibration displacement measurement methods, visual vibration measurement offers several advantages such as long-distance capability, non-contact operation and easy installation. However, the phenomenon of low fitting accuracy of the bounding box often occurs when detecting rotating objects, resulting in a slight deviation in the relative offset of the center vibration point of the target between frames, which will cause a serious deviation in the regression of vibration displacement offset. In this paper, a high-speed industrial camera is employed as the image acquisition medium, and a deep learning-based semantic segmentation method is introduced to address visual vibration measurement challenges in rotating body. Specifically, the CSP module integrates different depth semantic information which is introduced into the Mobiledets backbone network in a targeted manner. This is not only strengthens the performance of the network for segmenting vibration objects, but also dramatically improves the practical performance of the algorithm. The conventional Relu activation function is substituted with Mish activation function, making the network more adept at segmenting rotating body in challenging backgrounds with varying illumination, blur, and similarity. The CSP+Mobiledets backbone network constructed in this study outperforms the U-Net network in terms of feature extraction effectiveness. Adding Dice-loss to the original loss function can more effectively solve the severe imbalance problem of samples caused by long-distance image acquisition. We take the most representative rotating body-rotor as the experimental subject. The displacement curve obtained by the existing algorithm has the best degree of fit with the signal curve collected by the eddy sensor. The results of different segmentation algorithms and detection algorithms on time domain curve plot, frequency domain plot and axis orbit plot are collectively compared. Furthermore, the results also provide valuable guidance for visual measurement of the vibration displacement of the rotating body in specific industrial scenarios.
•A semantic segmentation network is applied for vibration displacement measurement.•CSP+Mobiledets backbone network to enhance feature extraction are proposed.•The proposed network has excellent performance under complex background.•The proposed algorithm has broad applicability on rotating structure. |
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| AbstractList | Compared with the traditional vibration displacement measurement methods, visual vibration measurement offers several advantages such as long-distance capability, non-contact operation and easy installation. However, the phenomenon of low fitting accuracy of the bounding box often occurs when detecting rotating objects, resulting in a slight deviation in the relative offset of the center vibration point of the target between frames, which will cause a serious deviation in the regression of vibration displacement offset. In this paper, a high-speed industrial camera is employed as the image acquisition medium, and a deep learning-based semantic segmentation method is introduced to address visual vibration measurement challenges in rotating body. Specifically, the CSP module integrates different depth semantic information which is introduced into the Mobiledets backbone network in a targeted manner. This is not only strengthens the performance of the network for segmenting vibration objects, but also dramatically improves the practical performance of the algorithm. The conventional Relu activation function is substituted with Mish activation function, making the network more adept at segmenting rotating body in challenging backgrounds with varying illumination, blur, and similarity. The CSP+Mobiledets backbone network constructed in this study outperforms the U-Net network in terms of feature extraction effectiveness. Adding Dice-loss to the original loss function can more effectively solve the severe imbalance problem of samples caused by long-distance image acquisition. We take the most representative rotating body-rotor as the experimental subject. The displacement curve obtained by the existing algorithm has the best degree of fit with the signal curve collected by the eddy sensor. The results of different segmentation algorithms and detection algorithms on time domain curve plot, frequency domain plot and axis orbit plot are collectively compared. Furthermore, the results also provide valuable guidance for visual measurement of the vibration displacement of the rotating body in specific industrial scenarios.
•A semantic segmentation network is applied for vibration displacement measurement.•CSP+Mobiledets backbone network to enhance feature extraction are proposed.•The proposed network has excellent performance under complex background.•The proposed algorithm has broad applicability on rotating structure. |
| ArticleNumber | 121306 |
| Author | Chai, Shanglei Liu, Xiaoqin Yang, Rongliang Liu, Tao Wang, Sen Liu, Chang |
| Author_xml | – sequence: 1 givenname: Shanglei orcidid: 0000-0002-0631-1438 surname: Chai fullname: Chai, Shanglei email: heitieya@126.com – sequence: 2 givenname: Sen orcidid: 0000-0003-1259-8030 surname: Wang fullname: Wang, Sen email: wangsen0401@126.com – sequence: 3 givenname: Chang surname: Liu fullname: Liu, Chang email: liuchang3385@gmail.com – sequence: 4 givenname: Xiaoqin surname: Liu fullname: Liu, Xiaoqin email: liuxqsmile@gmail.com – sequence: 5 givenname: Tao surname: Liu fullname: Liu, Tao email: liutao.hais@hotmail.com – sequence: 6 givenname: Rongliang surname: Yang fullname: Yang, Rongliang email: yrl9797@126.com |
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| Cites_doi | 10.1109/CVPR.2015.7298965 10.1109/CVPR.2019.00293 10.1016/j.jsv.2017.06.008 10.1016/j.eswa.2021.116027 10.1016/j.measurement.2017.09.043 10.1023/B:VISI.0000029664.99615.94 10.1016/j.ymssp.2022.109137 10.1016/j.eswa.2021.115565 10.1109/CVPR.2019.00584 10.1002/stc.2009 10.1109/CVPRW50498.2020.00203 10.1016/j.eswa.2009.10.041 10.1109/TNN.2002.1031944 10.1007/s13349-017-0261-4 10.1007/978-3-030-01234-2_49 10.1002/stc.1852 10.1016/j.measurement.2021.109847 10.1016/0004-3702(81)90024-2 10.3390/s17061305 10.1109/CVPR.2017.179 10.1080/15732479.2016.1164729 10.1016/j.eswa.2021.114570 10.1007/BF02310791 10.1109/CVPR46437.2021.00382 10.1002/stc.1850 10.1109/CVPR42600.2020.01079 10.1109/ICCV.2019.00140 10.1111/mice.12767 10.1016/j.eswa.2021.116290 10.1016/j.measurement.2016.12.020 10.1109/CVPR.2016.90 10.3390/s150716557 10.1109/CVPR.2017.660 10.1109/ICCV.2019.00667 10.1016/j.conbuildmat.2020.120923 |
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| Keywords | Deep learning Rotating body Semantic segmentation Vibration displacement measurement |
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| References | Xu, Brownjohn (b39) 2018; 8 Zhang, Liu, Zhao (b42) 2021; 267 (pp. 6569–6578). Rublee, Rabaud, Konolige, Bradski (b31) 2011 (pp. 2881–2890). Ma, Choi, Sohn (b26) 2022; 37 Li, Zheng, Li, Ma, Hu (b21) 2022; 190 Tan, Le (b35) 2019 (pp. 2820–2828). Horn, Schunck (b12) 1981; 17 Howard, A., Sandler, M., Chu, G., Chen, L., Chen, B., Tan, M., et al. (2019). Searching for mobilenetv3. In Khuc, Catbas (b19) 2017; 13 Zhu, Zhang, Lu, Li (b44) 2021; 183 Milletari, Navab, Ahmadi (b27) 2016 (pp. 1314–1324). Ren, He, Girshick, Sun (b29) 2015; 28 Sonkul, Dhage, Vyas (b32) 2021; 185 (pp. 801–818). Liu, Anguelov, Erhan, Szegedy, Reed, Fu (b22) 2016 Yoon, Elanwar, Choi, Golparvar-Fard, Spencer (b41) 2016; 23 Wang, C., Liao, H. M., Wu, Y., Chen, P., Hsieh, J., & Yeh, I. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. In Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid scene parsing network. In (pp. 3825–3834). Feng, Scarangello, Feng, Ye (b7) 2017; 99 (pp. 3431–3440). Khaloo, Lattanzi (b17) 2017; 24 Hu, He, Wang, Liu, Zhang, He (b14) 2017; 17 (pp. 2462–2470). Misra (b28) 2019 Sun, K., Xiao, B., Liu, D., & Wang, J. (2019). Deep high-resolution representation learning for human pose estimation. In He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Yang, Wang, Wu, Liu, Liu (b40) 2022; 177 Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., & Brox, T. (2017). Flownet 2.0: Evolution of optical flow estimation with deep networks. In Lucas, Kanade (b25) 1981 Ghate, Dudul (b10) 2010; 37 Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., & Tian, Q. (2019). Centernet: Keypoint triplets for object detection. In Khuc, Catbas (b18) 2017; 24 Feng, Feng, Ozer, Fukuda (b6) 2015; 15 Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Ge, Liu, Wang, Li, Sun (b9) 2021 (pp. 770–778). Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., et al. (2019). Mnasnet: Platform-aware neural architecture search for mobile. In Jin, Chen (b16) 2021; 171 Chen, L., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In Xiong, Y., Liu, H., Gupta, S., Akin, B., Bender, G., Wang, Y., et al. (2021). Mobiledets: Searching for object detection architectures for mobile accelerators. In (pp. 5693–5703). Dong, Ye, Jin (b3) 2018; 126 Carroll, Chang (b1) 1970; 35 (pp. 10781–10790). Feng, Feng (b5) 2017; 406 Lowe (b24) 2004; 60 Gautama, Van Hulle (b8) 2002; 13 Kumar, Hati (b20) 2022; 191 Tan, M., Pang, R., & Le, Q. V. (2020). Efficientdet: Scalable and efficient object detection. In Ronneberger, Fischer, Brox (b30) 2015 (pp. 390–391). Ghate (10.1016/j.eswa.2023.121306_b10) 2010; 37 Kumar (10.1016/j.eswa.2023.121306_b20) 2022; 191 10.1016/j.eswa.2023.121306_b43 Ren (10.1016/j.eswa.2023.121306_b29) 2015; 28 10.1016/j.eswa.2023.121306_b23 Sonkul (10.1016/j.eswa.2023.121306_b32) 2021; 185 Hu (10.1016/j.eswa.2023.121306_b14) 2017; 17 Xu (10.1016/j.eswa.2023.121306_b39) 2018; 8 Carroll (10.1016/j.eswa.2023.121306_b1) 1970; 35 Milletari (10.1016/j.eswa.2023.121306_b27) 2016 Misra (10.1016/j.eswa.2023.121306_b28) 2019 Liu (10.1016/j.eswa.2023.121306_b22) 2016 Feng (10.1016/j.eswa.2023.121306_b6) 2015; 15 Zhu (10.1016/j.eswa.2023.121306_b44) 2021; 183 Ge (10.1016/j.eswa.2023.121306_b9) 2021 Feng (10.1016/j.eswa.2023.121306_b7) 2017; 99 Horn (10.1016/j.eswa.2023.121306_b12) 1981; 17 Jin (10.1016/j.eswa.2023.121306_b16) 2021; 171 Dong (10.1016/j.eswa.2023.121306_b3) 2018; 126 Feng (10.1016/j.eswa.2023.121306_b5) 2017; 406 10.1016/j.eswa.2023.121306_b11 10.1016/j.eswa.2023.121306_b33 10.1016/j.eswa.2023.121306_b34 10.1016/j.eswa.2023.121306_b13 Gautama (10.1016/j.eswa.2023.121306_b8) 2002; 13 Li (10.1016/j.eswa.2023.121306_b21) 2022; 190 10.1016/j.eswa.2023.121306_b36 10.1016/j.eswa.2023.121306_b15 10.1016/j.eswa.2023.121306_b37 Tan (10.1016/j.eswa.2023.121306_b35) 2019 10.1016/j.eswa.2023.121306_b38 Yang (10.1016/j.eswa.2023.121306_b40) 2022; 177 Khuc (10.1016/j.eswa.2023.121306_b18) 2017; 24 Khuc (10.1016/j.eswa.2023.121306_b19) 2017; 13 Rublee (10.1016/j.eswa.2023.121306_b31) 2011 Khaloo (10.1016/j.eswa.2023.121306_b17) 2017; 24 Lucas (10.1016/j.eswa.2023.121306_b25) 1981 Ronneberger (10.1016/j.eswa.2023.121306_b30) 2015 10.1016/j.eswa.2023.121306_b2 Ma (10.1016/j.eswa.2023.121306_b26) 2022; 37 10.1016/j.eswa.2023.121306_b4 Zhang (10.1016/j.eswa.2023.121306_b42) 2021; 267 Lowe (10.1016/j.eswa.2023.121306_b24) 2004; 60 Yoon (10.1016/j.eswa.2023.121306_b41) 2016; 23 |
| References_xml | – reference: He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In – start-page: 2564 year: 2011 end-page: 2571 ident: b31 article-title: ORB: An efficient alternative to SIFT or SURF publication-title: 2011 International conference on computer vision – reference: (pp. 2462–2470). – volume: 190 year: 2022 ident: b21 article-title: One-shot neural architecture search for fault diagnosis using vibration signals publication-title: Expert Systems with Applications – reference: Wang, C., Liao, H. M., Wu, Y., Chen, P., Hsieh, J., & Yeh, I. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. In – volume: 406 start-page: 15 year: 2017 end-page: 28 ident: b5 article-title: Identification of structural stiffness and excitation forces in time domain using noncontact vision-based displacement measurement publication-title: Journal of Sound and Vibration – start-page: 565 year: 2016 end-page: 571 ident: b27 article-title: V-net: Fully convolutional neural networks for volumetric medical image segmentation publication-title: 2016 Fourth international conference on 3D vision – volume: 15 start-page: 16557 year: 2015 end-page: 16575 ident: b6 article-title: A vision-based sensor for noncontact structural displacement measurement publication-title: Sensors – volume: 24 year: 2017 ident: b17 article-title: Pixel-wise structural motion tracking from rectified repurposed videos publication-title: Structural Control and Health Monitoring – volume: 37 start-page: 3468 year: 2010 end-page: 3481 ident: b10 article-title: Optimal MLP neural network classifier for fault detection of three phase induction motor publication-title: Expert Systems with Applications – reference: (pp. 2881–2890). – volume: 17 start-page: 1305 year: 2017 ident: b14 article-title: A high-speed target-free vision-based sensor for bus rapid transit viaduct vibration measurements using CMT and ORB algorithms publication-title: Sensors – volume: 17 start-page: 185 year: 1981 end-page: 203 ident: b12 article-title: Determining optical flow publication-title: Artificial Intelligence – reference: (pp. 3825–3834). – volume: 24 year: 2017 ident: b18 article-title: Completely contactless structural health monitoring of real-life structures using cameras and computer vision publication-title: Structural Control and Health Monitoring – reference: Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., et al. (2019). Mnasnet: Platform-aware neural architecture search for mobile. In – year: 1981 ident: b25 publication-title: An iterative image registration technique with an application to stereo vision – volume: 99 start-page: 44 year: 2017 end-page: 52 ident: b7 article-title: Cable tension force estimate using novel noncontact vision-based sensor publication-title: Measurement – reference: Chen, L., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In – reference: (pp. 5693–5703). – reference: (pp. 801–818). – start-page: 10 year: 2019 end-page: 48550 ident: b28 article-title: Mish: A self regularized non-monotonic neural activation function – reference: Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., & Brox, T. (2017). Flownet 2.0: Evolution of optical flow estimation with deep networks. In – volume: 126 start-page: 405 year: 2018 end-page: 416 ident: b3 article-title: Identification of structural dynamic characteristics based on machine vision technology publication-title: Measurement – volume: 35 start-page: 283 year: 1970 end-page: 319 ident: b1 article-title: Analysis of individual differences in multidimensional scaling via an N-way generalization of “eckart-Young” decomposition publication-title: Psychometrika – volume: 191 year: 2022 ident: b20 article-title: Dilated convolutional neural network based model for bearing faults and broken rotor bar detection in squirrel cage induction motors publication-title: Expert Systems with Applications – reference: Sun, K., Xiao, B., Liu, D., & Wang, J. (2019). Deep high-resolution representation learning for human pose estimation. In – reference: Xiong, Y., Liu, H., Gupta, S., Akin, B., Bender, G., Wang, Y., et al. (2021). Mobiledets: Searching for object detection architectures for mobile accelerators. In – volume: 13 start-page: 1127 year: 2002 end-page: 1136 ident: b8 article-title: A phase-based approach to the estimation of the optical flow field using spatial filtering publication-title: IEEE Transactions on Neural Networks – volume: 183 year: 2021 ident: b44 article-title: A multi-resolution deep feature framework for dynamic displacement measurement of bridges using vision-based tracking system publication-title: Measurement – volume: 171 year: 2021 ident: b16 article-title: An end-to-end framework combining time–frequency expert knowledge and modified transformer networks for vibration signal classification publication-title: Expert Systems with Applications – reference: Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., & Tian, Q. (2019). Centernet: Keypoint triplets for object detection. In – reference: (pp. 2820–2828). – reference: Tan, M., Pang, R., & Le, Q. V. (2020). Efficientdet: Scalable and efficient object detection. In – volume: 13 start-page: 505 year: 2017 end-page: 516 ident: b19 article-title: Computer vision-based displacement and vibration monitoring without using physical target on structures publication-title: Structure and Infrastructure Engineering – volume: 185 year: 2021 ident: b32 article-title: Single and multi-label fault classification in rotors from unprocessed multi-sensor data through deep and parallel CNN architectures publication-title: Expert Systems with Applications – volume: 28 year: 2015 ident: b29 article-title: Faster r-cnn: Towards real-time object detection with region proposal networks publication-title: Advances in Neural Information Processing Systems – volume: 23 start-page: 1405 year: 2016 end-page: 1416 ident: b41 article-title: Target-free approach for vision-based structural system identification using consumer-grade cameras publication-title: Structural Control and Health Monitoring – start-page: 21 year: 2016 end-page: 37 ident: b22 article-title: Ssd: Single shot multibox detector publication-title: European conference on computer vision – reference: (pp. 6569–6578). – reference: Howard, A., Sandler, M., Chu, G., Chen, L., Chen, B., Tan, M., et al. (2019). Searching for mobilenetv3. In – reference: (pp. 390–391). – year: 2021 ident: b9 article-title: Yolox: Exceeding yolo series in 2021 – reference: (pp. 770–778). – start-page: 234 year: 2015 end-page: 241 ident: b30 article-title: U-net: Convolutional networks for biomedical image segmentation publication-title: International conference on medical image computing and computer-assisted intervention – volume: 177 year: 2022 ident: b40 article-title: Using lightweight convolutional neural network to track vibration displacement in rotating body video publication-title: Mechanical Systems and Signal Processing – reference: (pp. 10781–10790). – volume: 8 start-page: 91 year: 2018 end-page: 110 ident: b39 article-title: Review of machine-vision based methodologies for displacement measurement in civil structures publication-title: Journal of Civil Structural Health Monitoring – volume: 60 start-page: 91 year: 2004 end-page: 110 ident: b24 article-title: Distinctive image features from scale-invariant keypoints publication-title: International Journal of Computer Vision – volume: 267 year: 2021 ident: b42 article-title: Structural displacement monitoring based on mask regions with convolutional neural network publication-title: Construction and Building Materials – reference: (pp. 3431–3440). – reference: Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017). Pyramid scene parsing network. In – reference: (pp. 1314–1324). – start-page: 6105 year: 2019 end-page: 6114 ident: b35 article-title: Efficientnet: Rethinking model scaling for convolutional neural networks publication-title: International conference on machine learning – volume: 37 start-page: 688 year: 2022 end-page: 703 ident: b26 article-title: Real-time structural displacement estimation by fusing asynchronous acceleration and computer vision measurements publication-title: Computer-Aided Civil and Infrastructure Engineering – reference: Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In – ident: 10.1016/j.eswa.2023.121306_b23 doi: 10.1109/CVPR.2015.7298965 – start-page: 565 year: 2016 ident: 10.1016/j.eswa.2023.121306_b27 article-title: V-net: Fully convolutional neural networks for volumetric medical image segmentation – ident: 10.1016/j.eswa.2023.121306_b34 doi: 10.1109/CVPR.2019.00293 – volume: 406 start-page: 15 year: 2017 ident: 10.1016/j.eswa.2023.121306_b5 article-title: Identification of structural stiffness and excitation forces in time domain using noncontact vision-based displacement measurement publication-title: Journal of Sound and Vibration doi: 10.1016/j.jsv.2017.06.008 – volume: 190 year: 2022 ident: 10.1016/j.eswa.2023.121306_b21 article-title: One-shot neural architecture search for fault diagnosis using vibration signals publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.116027 – volume: 126 start-page: 405 year: 2018 ident: 10.1016/j.eswa.2023.121306_b3 article-title: Identification of structural dynamic characteristics based on machine vision technology publication-title: Measurement doi: 10.1016/j.measurement.2017.09.043 – start-page: 21 year: 2016 ident: 10.1016/j.eswa.2023.121306_b22 article-title: Ssd: Single shot multibox detector – start-page: 6105 year: 2019 ident: 10.1016/j.eswa.2023.121306_b35 article-title: Efficientnet: Rethinking model scaling for convolutional neural networks – volume: 60 start-page: 91 year: 2004 ident: 10.1016/j.eswa.2023.121306_b24 article-title: Distinctive image features from scale-invariant keypoints publication-title: International Journal of Computer Vision doi: 10.1023/B:VISI.0000029664.99615.94 – volume: 177 year: 2022 ident: 10.1016/j.eswa.2023.121306_b40 article-title: Using lightweight convolutional neural network to track vibration displacement in rotating body video publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2022.109137 – volume: 185 year: 2021 ident: 10.1016/j.eswa.2023.121306_b32 article-title: Single and multi-label fault classification in rotors from unprocessed multi-sensor data through deep and parallel CNN architectures publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.115565 – ident: 10.1016/j.eswa.2023.121306_b33 doi: 10.1109/CVPR.2019.00584 – volume: 24 year: 2017 ident: 10.1016/j.eswa.2023.121306_b17 article-title: Pixel-wise structural motion tracking from rectified repurposed videos publication-title: Structural Control and Health Monitoring doi: 10.1002/stc.2009 – ident: 10.1016/j.eswa.2023.121306_b37 doi: 10.1109/CVPRW50498.2020.00203 – volume: 37 start-page: 3468 year: 2010 ident: 10.1016/j.eswa.2023.121306_b10 article-title: Optimal MLP neural network classifier for fault detection of three phase induction motor publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2009.10.041 – volume: 13 start-page: 1127 year: 2002 ident: 10.1016/j.eswa.2023.121306_b8 article-title: A phase-based approach to the estimation of the optical flow field using spatial filtering publication-title: IEEE Transactions on Neural Networks doi: 10.1109/TNN.2002.1031944 – volume: 8 start-page: 91 year: 2018 ident: 10.1016/j.eswa.2023.121306_b39 article-title: Review of machine-vision based methodologies for displacement measurement in civil structures publication-title: Journal of Civil Structural Health Monitoring doi: 10.1007/s13349-017-0261-4 – ident: 10.1016/j.eswa.2023.121306_b2 doi: 10.1007/978-3-030-01234-2_49 – volume: 24 year: 2017 ident: 10.1016/j.eswa.2023.121306_b18 article-title: Completely contactless structural health monitoring of real-life structures using cameras and computer vision publication-title: Structural Control and Health Monitoring doi: 10.1002/stc.1852 – volume: 183 year: 2021 ident: 10.1016/j.eswa.2023.121306_b44 article-title: A multi-resolution deep feature framework for dynamic displacement measurement of bridges using vision-based tracking system publication-title: Measurement doi: 10.1016/j.measurement.2021.109847 – year: 1981 ident: 10.1016/j.eswa.2023.121306_b25 – volume: 17 start-page: 185 year: 1981 ident: 10.1016/j.eswa.2023.121306_b12 article-title: Determining optical flow publication-title: Artificial Intelligence doi: 10.1016/0004-3702(81)90024-2 – volume: 17 start-page: 1305 year: 2017 ident: 10.1016/j.eswa.2023.121306_b14 article-title: A high-speed target-free vision-based sensor for bus rapid transit viaduct vibration measurements using CMT and ORB algorithms publication-title: Sensors doi: 10.3390/s17061305 – ident: 10.1016/j.eswa.2023.121306_b15 doi: 10.1109/CVPR.2017.179 – volume: 13 start-page: 505 year: 2017 ident: 10.1016/j.eswa.2023.121306_b19 article-title: Computer vision-based displacement and vibration monitoring without using physical target on structures publication-title: Structure and Infrastructure Engineering doi: 10.1080/15732479.2016.1164729 – start-page: 2564 year: 2011 ident: 10.1016/j.eswa.2023.121306_b31 article-title: ORB: An efficient alternative to SIFT or SURF – volume: 171 year: 2021 ident: 10.1016/j.eswa.2023.121306_b16 article-title: An end-to-end framework combining time–frequency expert knowledge and modified transformer networks for vibration signal classification publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.114570 – volume: 35 start-page: 283 year: 1970 ident: 10.1016/j.eswa.2023.121306_b1 article-title: Analysis of individual differences in multidimensional scaling via an N-way generalization of “eckart-Young” decomposition publication-title: Psychometrika doi: 10.1007/BF02310791 – ident: 10.1016/j.eswa.2023.121306_b38 doi: 10.1109/CVPR46437.2021.00382 – volume: 23 start-page: 1405 year: 2016 ident: 10.1016/j.eswa.2023.121306_b41 article-title: Target-free approach for vision-based structural system identification using consumer-grade cameras publication-title: Structural Control and Health Monitoring doi: 10.1002/stc.1850 – ident: 10.1016/j.eswa.2023.121306_b36 doi: 10.1109/CVPR42600.2020.01079 – ident: 10.1016/j.eswa.2023.121306_b13 doi: 10.1109/ICCV.2019.00140 – volume: 37 start-page: 688 year: 2022 ident: 10.1016/j.eswa.2023.121306_b26 article-title: Real-time structural displacement estimation by fusing asynchronous acceleration and computer vision measurements publication-title: Computer-Aided Civil and Infrastructure Engineering doi: 10.1111/mice.12767 – start-page: 10 year: 2019 ident: 10.1016/j.eswa.2023.121306_b28 – start-page: 234 year: 2015 ident: 10.1016/j.eswa.2023.121306_b30 article-title: U-net: Convolutional networks for biomedical image segmentation – volume: 28 year: 2015 ident: 10.1016/j.eswa.2023.121306_b29 article-title: Faster r-cnn: Towards real-time object detection with region proposal networks publication-title: Advances in Neural Information Processing Systems – volume: 191 year: 2022 ident: 10.1016/j.eswa.2023.121306_b20 article-title: Dilated convolutional neural network based model for bearing faults and broken rotor bar detection in squirrel cage induction motors publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.116290 – volume: 99 start-page: 44 year: 2017 ident: 10.1016/j.eswa.2023.121306_b7 article-title: Cable tension force estimate using novel noncontact vision-based sensor publication-title: Measurement doi: 10.1016/j.measurement.2016.12.020 – year: 2021 ident: 10.1016/j.eswa.2023.121306_b9 – ident: 10.1016/j.eswa.2023.121306_b11 doi: 10.1109/CVPR.2016.90 – volume: 15 start-page: 16557 year: 2015 ident: 10.1016/j.eswa.2023.121306_b6 article-title: A vision-based sensor for noncontact structural displacement measurement publication-title: Sensors doi: 10.3390/s150716557 – ident: 10.1016/j.eswa.2023.121306_b43 doi: 10.1109/CVPR.2017.660 – ident: 10.1016/j.eswa.2023.121306_b4 doi: 10.1109/ICCV.2019.00667 – volume: 267 year: 2021 ident: 10.1016/j.eswa.2023.121306_b42 article-title: Structural displacement monitoring based on mask regions with convolutional neural network publication-title: Construction and Building Materials doi: 10.1016/j.conbuildmat.2020.120923 |
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