{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T04:58:46Z","timestamp":1790398726814,"version":"4.1.0"},"reference-count":35,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52270159"],"award-info":[{"award-number":["52270159"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["52270159"],"award-info":[{"award-number":["52270159"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate characterization of particle dynamics in complex swirling flows is essential for understanding flow behavior and transport mechanisms. However, extracting three-dimensional (3D) rotational motion parameters of particles from High-Speed Motion Analyzer (HSMA) imaging experiments remains challenging due to the limitations of conventional manual frame-by-frame analysis, which suffers from low efficiency, subjective errors, and poor scalability. In this study, an intelligent imaging-based sensing system is proposed for automated 3D measurement of coupled particle self-rotation and revolution motion in hydrocyclone swirling fields. The proposed system integrates dual orthogonal high-speed cameras with a machine learning-driven processing framework, enabling an end-to-end workflow consisting of particle detection, multi-object tracking, motion-state recognition, kinematic parameter calculation. To achieve robust particle detection under different imaging conditions, You Only Look Once version 8 (YOLOv8) detectors are trained following a mixed-view strategy: the Y-view detector is trained on the Y-camera data, whereas the X-view detector is trained on the merged dual-camera data. ByteTrack is subsequently employed to associate particle detections across consecutive frames and generate continuous trajectories. For motion-state recognition, EfficientNet-B1 is adopted to classify the relative positional states of internal particle markers. Furthermore, a Self-Attention Generative Adversarial Network (SAGAN) is introduced for minority-class sample generation to alleviate class imbalance and improve classification robustness.Based on the extracted trajectories and state information, a kinematic model is integrated to automatically estimate particle revolution and self-rotation velocities. Experimental results demonstrate that the proposed YOLOv8 detectors achieve reliable detection performance, with mean average precision at an Intersection over Union (IoU) threshold of 0.5 (mAP@0.5) values of 79.84% and 71.62% for the two imaging views, respectively. The combined YOLOv8-ByteTrack framework achieves Higher Order Tracking Accuracy (HOTA) and Multiple Object Tracking Accuracy (MOTA) values of 63.791 and 90.49, respectively. After SAGAN-based data augmentation, the classification accuracy of EfficientNet-B1 increases from 98.62% to 99.54%. System-level validation using more than 60 randomly selected particle trajectories shows average measurement accuracies of 97.59% for revolution velocity and 91.8% for self-rotation velocity, while reducing the processing time from more than 600 s manually to 11.1 s per trajectory. The proposed machine learning-assisted particle 3D motion sensing system provides an efficient and reliable solution for automated extraction of particle kinematic information under complex flow imaging conditions, offering new opportunities for intelligent monitoring and mechanism analysis of multiphase flow systems.<\/jats:p>","DOI":"10.3390\/s26196063","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:26:11Z","timestamp":1790256371000},"page":"6063","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Assisted Measurement of Three-Dimensional Particle Self-Rotation and Revolution in Swirling Flow Fields"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3341-0844","authenticated-orcid":false,"given":"Fengqin","family":"He","sequence":"first","affiliation":[{"name":"The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0914-7183","authenticated-orcid":false,"given":"Weiqing","family":"Liu","sequence":"additional","affiliation":[{"name":"The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8302-9583","authenticated-orcid":false,"given":"Yuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Environmental Pollution and Health, School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qibin","family":"Liu","sequence":"additional","affiliation":[{"name":"The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China"},{"name":"Shangtex Investment & Trading Co., Ltd., Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"124933","DOI":"10.1016\/j.watres.2025.124933","article-title":"Enhancing Oil-Water Separation via Droplet Coalescence Regulated by Particle Self-Rotation in Swirling Fields","volume":"289","author":"Zhao","year":"2026","journal-title":"Water Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"123198","DOI":"10.1016\/j.watres.2025.123198","article-title":"Hydrogen Production by Suspension Self-Rotation Enhanced Pyrolysis of Sludge Particles in Cyclone","volume":"275","author":"Jiang","year":"2025","journal-title":"Water Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"134452","DOI":"10.1016\/j.energy.2025.134452","article-title":"Energy-Efficient Design of Cyclone Separators: Machine Learning Prediction of Particle Self-Rotation Velocities","volume":"316","author":"Zhang","year":"2025","journal-title":"Energy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"136456","DOI":"10.1016\/j.jhazmat.2024.136456","article-title":"Hydrocyclone-Induced Classification Reduction and Spin Desorption of Contaminated Soil Remediation with Heavy Metal and Organics","volume":"480","author":"Liu","year":"2024","journal-title":"J. Hazard. Mater."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ekechukwu, O.M., Asim, T., and Hawez, H.K. (2024). Recent Developments in Hydrocyclone Technology for Oil-in-Water Separation from Produced Water. Energies, 17.","DOI":"10.3390\/en17133181"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.powtec.2007.04.007","article-title":"Experimental Study of Particle Rotation Characteristics with High-Speed Digital Imaging System","volume":"181","author":"Wu","year":"2008","journal-title":"Powder Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.seppur.2018.03.059","article-title":"Deoiling of Oil-Coated Catalyst Using High-Speed Suspending Self-Rotation in Cyclone","volume":"210","author":"Shi","year":"2019","journal-title":"Sep. Purif. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"126909","DOI":"10.1016\/j.cej.2020.126909","article-title":"Cold Model Testing of In-Situ Catalyst Activation by Swirling Self-Rotation in Ebullated Bed Reactor for Biomass Pyrolysis Oils Hydrogenation","volume":"406","author":"Li","year":"2021","journal-title":"Chem. Eng. J."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, A., Wu, H., and Iwahori, Y. (2025). Advances in Computer Vision and Deep Learning and Its Applications. Electronics, 14.","DOI":"10.3390\/electronics14081551"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mirzaei, B., Nezamabadi-pour, H., Raoof, A., and Derakhshani, R. (2023). Small Object Detection and Tracking: A Comprehensive Review. Sensors, 23.","DOI":"10.3390\/s23156887"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, D., Wang, J.G., and Xu, K. (2021). Deep Learning for Object Detection, Classification and Tracking in Industry Applications. Sensors, 21.","DOI":"10.3390\/s21217349"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., and Wang, X. (2022). ByteTrack: Multi-Object Tracking by Associating Every Detection Box. Proceedings of the European Conference on Computer Vision (ECCV 2022), Tel Aviv, Israel, 23\u201327 October 2022, Springer.","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.seppur.2016.12.001","article-title":"High-Speed Particle Rotation for Coating Oil Removal by Hydrocyclone","volume":"177","author":"Huang","year":"2017","journal-title":"Sep. Purif. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27\u201330 June 2016, IEEE.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yurdakul, M., Sazak, H., Kotan, M., and Ta\u015fdemir, \u015e. (2026). A Review of YOLO Family from YOLOv1 to YOLO26. Preprints, 2026021844.","DOI":"10.20944\/preprints202602.1844.v1"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. Proceedings of the Advances in Neural Information Processing Systems 37, Vancouver, BC, Canada, 10\u201315 December 2024, Neural Information Processing Systems Foundation, Inc. (NeurIPS).","DOI":"10.52202\/079017-3429"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016). Simple Online and Realtime Tracking. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 25\u201328 September 2016, IEEE.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Harefa, M.T., and Wijaya, D.R. (2025). A Real-Time Multi-Object Tracker Based on Hybrid Integration of ByteTrack and DeepSORT Using YOLOv8. Proceedings of the 2025 IEEE International Conference on Artificial Intelligence for Learning and Optimization (ICoAILO), Bali, Indonesia, 7\u20139 August 2025, IEEE.","DOI":"10.1109\/ICoAILO66760.2025.11155979"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111151","DOI":"10.1016\/j.compag.2025.111151","article-title":"Depth-OC-SORT-based Multi-Object Tracking of Cattle in Real-World Farming Scenarios","volume":"240","author":"Zhu","year":"2026","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cao, J., Pang, J., Weng, X., Khirodkar, R., and Kitani, K. (2023). Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 17\u201324 June 2023, IEEE.","DOI":"10.1109\/CVPR52729.2023.00934"},{"key":"ref_21","first-page":"12993","article-title":"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression","volume":"34","author":"Zheng","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_22","unstructured":"Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., and Yang, J. (2020). Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, M., Liu, X., Chen, S., Yang, L., Du, Q., Han, Z., and Wang, J. (2024). MST-YOLO: Small Object Detection Model for Autonomous Driving. Sensors, 24.","DOI":"10.3390\/s24227347"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1007\/s11263-020-01375-2","article-title":"HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking","volume":"129","author":"Luiten","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"246309","DOI":"10.1155\/2008\/246309","article-title":"Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics","volume":"2008","author":"Bernardin","year":"2008","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27\u201330 June 2016, IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_28","first-page":"12581","article-title":"Gated Convolutional Networks with Hybrid Connectivity for Image Classification","volume":"34","author":"Yang","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_29","unstructured":"Tan, M., and Le, Q. (2019). EfficientNet: Rethinking Model Scaling for Convolutional NeuralNetworks. arXiv."},{"key":"ref_30","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2018). Self-Attention Generative Adversarial Networks. arXiv."},{"key":"ref_31","first-page":"2672","article-title":"Generative Adversarial Nets","volume":"Volume 27","author":"Goodfellow","year":"2014","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_32","unstructured":"Tan, M., and Le, Q. (2021). EfficientNetV2: Smaller Models and Faster Training. Proceedings of the 38th International Conference on Machine Learning, Online, 18\u201324 July 2021, PMLR."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"140019","DOI":"10.1016\/j.seppur.2026.140019","article-title":"Mechanisms of Pore-Scale Liquid Migration during Cyclonic Self-Rotation Drying of Sludge: Effects of Self-Rotation Speed and Throat Structure","volume":"416","author":"Zhu","year":"2026","journal-title":"Sep. Purif. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"148012","DOI":"10.1016\/j.jclepro.2026.148012","article-title":"In-Situ Reactivation of the Catalyst for Hydrodeoxygenation of Bio-Oil to Biofuel by Coupling the Self-Rotation and Revolution of Catalysts in the Swirling Flow","volume":"553","author":"Zhang","year":"2026","journal-title":"J. Clean. Prod."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Zheng, K., Lu, Z., Tao, P., Liu, J., Jiang, Z., Wei, A., Wang, H., and Fu, P. (2026). Mechanisms of Sludge Dewatering in Non-Phase-Change Cyclonic Drying: Effects of Self-Rotation Speed and Pore-Throat Structure. SSRN.","DOI":"10.2139\/ssrn.7126929"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6063\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T04:11:55Z","timestamp":1790395915000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/6063"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,24]]},"references-count":35,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26196063"],"URL":"https:\/\/doi.org\/10.3390\/s26196063","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,24]]}}}