{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T14:37:59Z","timestamp":1779374279043,"version":"3.53.1"},"reference-count":64,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EURAMET program","award":["22DIT01-ViDiT"],"award-info":[{"award-number":["22DIT01-ViDiT"]}]},{"name":"EURAMET program","award":["23IND08-DiVision"],"award-info":[{"award-number":["23IND08-DiVision"]}]},{"name":"European Union\u2019s Horizon 2020 research and innovation program","award":["22DIT01-ViDiT"],"award-info":[{"award-number":["22DIT01-ViDiT"]}]},{"name":"European Union\u2019s Horizon 2020 research and innovation program","award":["23IND08-DiVision"],"award-info":[{"award-number":["23IND08-DiVision"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the realm of computer vision, the integration of advanced techniques into the pre-processing of RGB-D camera inputs poses a significant challenge, given the inherent complexities arising from diverse environmental conditions and varying object appearances. Therefore, this paper introduces FusionVision, an exhaustive pipeline adapted for the robust 3D segmentation of objects in RGB-D imagery. Traditional computer vision systems face limitations in simultaneously capturing precise object boundaries and achieving high-precision object detection on depth maps, as they are mainly proposed for RGB cameras. To address this challenge, FusionVision adopts an integrated approach by merging state-of-the-art object detection techniques, with advanced instance segmentation methods. The integration of these components enables a holistic (unified analysis of information obtained from both color RGB and depth D channels) interpretation of RGB-D data, facilitating the extraction of comprehensive and accurate object information in order to improve post-processes such as object 6D pose estimation, Simultanious Localization and Mapping (SLAM) operations, accurate 3D dataset extraction, etc. The proposed FusionVision pipeline employs YOLO for identifying objects within the RGB image domain. Subsequently, FastSAM, an innovative semantic segmentation model, is applied to delineate object boundaries, yielding refined segmentation masks. The synergy between these components and their integration into 3D scene understanding ensures a cohesive fusion of object detection and segmentation, enhancing overall precision in 3D object segmentation.<\/jats:p>","DOI":"10.3390\/s24092889","type":"journal-article","created":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T03:30:49Z","timestamp":1714534249000},"page":"2889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["FusionVision: A Comprehensive Approach of 3D Object Reconstruction and Segmentation from RGB-D Cameras Using YOLO and Fast Segment Anything"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5403-3911","authenticated-orcid":false,"given":"Safouane","family":"El Ghazouali","sequence":"first","affiliation":[{"name":"TOELT LLC, AI Lab, 8406 Winterthur, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youssef","family":"Mhirit","sequence":"additional","affiliation":[{"name":"Independent Researcher, 75000 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Oukhrid","sequence":"additional","affiliation":[{"name":"Independent Researcher, 2502 Biel\/Bienne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6060-5365","authenticated-orcid":false,"given":"Umberto","family":"Michelucci","sequence":"additional","affiliation":[{"name":"TOELT LLC, AI Lab, 8406 Winterthur, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8696-342X","authenticated-orcid":false,"given":"Hichem","family":"Nouira","sequence":"additional","affiliation":[{"name":"LNE Laboratoire National de Metrologie et d\u2019Essaies, 75015 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1109\/TCYB.2015.2430526","article-title":"Robotic Online Path Planning on Point Cloud","volume":"46","author":"Liu","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ding, Z., Sun, Y., Xu, S., Pan, Y., Peng, Y., and Mao, Z. (2023). Recent Advances and Perspectives in Deep Learning Techniques for 3D Point Cloud Data Processing. Robotics, 12.","DOI":"10.3390\/robotics12040100"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109444","DOI":"10.1016\/j.patcog.2023.109444","article-title":"Segmentation of 3D Point Cloud Data Representing Full Human Body Geometry: A Review","volume":"139","author":"Krawczyk","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_4","unstructured":"Wu, F., Qian, Y., Zheng, H., Zhang, Y., and Zheng, X. (September, January 28). A Novel Neighbor Aggregation Function for Medical Point Cloud Analysis. Proceedings of the Computer Graphics International Conference, Shanghai, China."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xie, X., Wei, H., and Yang, Y. (2023). Real-Time LiDAR Point-Cloud Moving Object Segmentation for Autonomous Driving. Sensors, 23.","DOI":"10.3390\/s23010547"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, K., Bao, H., Zheng, Y., and Yang, Y. (2023). PMPF: Point-Cloud Multiple-Pixel Fusion-Based 3D Object Detection for Autonomous Driving. Remote Sens., 15.","DOI":"10.3390\/rs15061580"},{"key":"ref_7","first-page":"1","article-title":"Extraction of a floor plan from a points cloud: Some metrological considerations","volume":"12","author":"Chiominto","year":"2023","journal-title":"Acta IMEKO"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1007\/s00170-022-10576-7","article-title":"Applications of data fusion in optical coordinate metrology: A review","volume":"124","author":"Zhang","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Altuntas, C. (2023). Review of Scanning and Pixel Array-Based LiDAR Point-Cloud Measurement Techniques to Capture 3D Shape or Motion. Appl. Sci., 13.","DOI":"10.3390\/app13116488"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108035","DOI":"10.1016\/j.compag.2023.108035","article-title":"RGB-D datasets for robotic perception in site-specific agricultural operations\u2014A survey","volume":"212","author":"Kurtser","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112487","DOI":"10.1016\/j.measurement.2023.112487","article-title":"Robust Depth-Aided RGBD-Inertial Odometry for Indoor Localization","volume":"209","author":"Zhao","year":"2023","journal-title":"Measurement"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s10462-022-10176-7","article-title":"Deep learning for video object segmentation: A review","volume":"56","author":"Gao","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e2","DOI":"10.1561\/116.00000140","article-title":"A Survey of Efficient Deep Learning Models for Moving Object Segmentation","volume":"12","author":"Hou","year":"2023","journal-title":"APSIPA Trans. Signal Inf. Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"21353","DOI":"10.1007\/s11042-022-13801-3","article-title":"A survey: Object detection methods from CNN to transformer","volume":"82","author":"Arkin","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103812","DOI":"10.1016\/j.dsp.2022.103812","article-title":"A comprehensive review of object detection with deep learning","volume":"132","author":"Kaur","year":"2023","journal-title":"Digit. Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3997","DOI":"10.1016\/j.matpr.2021.02.533","article-title":"Object detection through region proposal based techniques","volume":"46","year":"2021","journal-title":"Mater. Today Proc."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_18","unstructured":"Redmon, J., Divvala, S.K., Girshick, R.B., and Farhadi, A. (July, January 26). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1561\/0600000097","article-title":"A Comprehensive Review of Modern Object Segmentation Approaches","volume":"13","author":"Wang","year":"2022","journal-title":"Found. Trends\u00ae Comput. Graph. Vis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1007\/s10462-018-9641-3","article-title":"Recent progress in semantic image segmentation","volume":"52","author":"Liu","year":"2018","journal-title":"Artif. Intell. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s13735-020-00195-x","article-title":"A survey on instance segmentation: State of the art","volume":"9","author":"Hafiz","year":"2020","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"ref_22","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2024, January 15). Ultralytics YOLOv8. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1007\/s10462-022-10213-5","article-title":"A review of convolutional neural network architectures and their optimizations","volume":"56","author":"Cong","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Luo, Z., Fang, Z., Zheng, S., Wang, Y., and Fu, Y. (2021, January 21\u201324). NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection. Proceedings of the 2021 International Conference on Multimedia Retrieval, Taipei, Taiwan.","DOI":"10.1145\/3460426.3463588"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023, January 2\u20136). Segment Anything. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_26","first-page":"2173","article-title":"Application of U-Net and Optimized Clustering in Medical Image Segmentation: A Review","volume":"136","author":"Shao","year":"2023","journal-title":"CMES-Comput. Model. Eng. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107511","DOI":"10.1016\/j.compag.2022.107511","article-title":"Modified U-Net for plant diseased leaf image segmentation","volume":"204","author":"Zhang","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Aghdam, E.K., Azad, R., Zarvani, M., and Merhof, D. (2023, January 17\u201321). Attention swin u-net: Cross-contextual attention mechanism for skin lesion segmentation. Proceedings of the 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia.","DOI":"10.1109\/ISBI53787.2023.10230337"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_30","unstructured":"He, S., Bao, R., Li, J., Stout, J., Bjornerud, A., Grant, P.E., and Ou, Y. (2023). Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets. arXiv."},{"key":"ref_31","unstructured":"Jiang, P.T., and Yang, Y. (2023). Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation. arXiv."},{"key":"ref_32","first-page":"103540","article-title":"The segment anything model (sam) for remote sensing applications: From zero to one shot","volume":"124","author":"Osco","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3699","DOI":"10.1038\/s41598-023-30409-1","article-title":"The research of a novel WOG-YOLO algorithm for autonomous driving object detection","volume":"13","author":"Xu","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Qureshi, R., Ragab, M.G., Abdulkader, S.J., Alqushaib, A., Sumiea, E.H., and Alhussian, H. (2023). A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023), Authorea Preprints.","DOI":"10.36227\/techrxiv.23681679"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"104302","DOI":"10.1016\/j.jdent.2022.104302","article-title":"Accuracy of RGB-D camera-based and stereophotogrammetric facial scanners: A comparative study","volume":"127","author":"Pan","year":"2022","journal-title":"J. Dent."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yan, S., Yang, J., K\u00e4pyl\u00e4, J., Zheng, F., Leonardis, A., and K\u00e4m\u00e4r\u00e4inen, J. (2021, January 11\u201317). DepthTrack: Unveiling the Power of RGBD Tracking. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01055"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"401","DOI":"10.3390\/digital2030022","article-title":"On 3D Reconstruction Using RGB-D Cameras","volume":"2","author":"Tychola","year":"2022","journal-title":"Digital"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s41095-021-0250-8","article-title":"High-quality indoor scene 3D reconstruction with RGB-D cameras: A brief review","volume":"8","author":"Li","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3495","DOI":"10.3233\/JIFS-169287","article-title":"Dynamic hand gesture recognition using RGB-D data for natural human-computer interaction","volume":"32","author":"Linqin","year":"2017","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_40","unstructured":"Gao, W., and Miao, P. (2018, January 16\u201317). RGB-D Camera Assists Virtual Studio through Human Computer Interaction. Proceedings of the 2018 3rd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2018), Chennai, India."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1177\/0278364917713117","article-title":"RGB-D object detection and semantic segmentation for autonomous manipulation in clutter","volume":"37","author":"Schwarz","year":"2018","journal-title":"Int. J. Robot. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.cviu.2016.03.019","article-title":"RGB-D camera based wearable navigation system for the visually impaired","volume":"149","author":"Lee","year":"2016","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1109\/TRO.2013.2279412","article-title":"3-D mapping with an RGB-D camera","volume":"30","author":"Endres","year":"2013","journal-title":"IEEE Trans. Robot."},{"key":"ref_44","unstructured":"Lai, K., Bo, L., Ren, X., and Fox, D. (2013). Consumer Depth Cameras for Computer Vision: Research Topics and Applications, Springer."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Prankl, J., Aldoma, A., Svejda, A., and Vincze, M. (October, January 28). RGB-D object modelling for object recognition and tracking. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent robots And Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353360"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Gen\u00e9-Mola, J., Llorens, J., Rosell-Polo, J.R., Gregorio, E., Arn\u00f3, J., Solanelles, F., Mart\u00ednez-Casasnovas, J.A., and Escol\u00e0, A. (2020). Assessing the Performance of RGB-D Sensors for 3D Fruit Crop Canopy Characterization under Different Operating and Lighting Conditions. Sensors, 20.","DOI":"10.3390\/s20247072"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xu, S., and Zell, A. (2020, January 22\u201324). Real-time 3D Object Detection from Point Clouds using an RGB-D Camera. Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods\u2014Volume 1: ICPRAM, INSTICC, Valletta, Malta.","DOI":"10.5220\/0008918904070414"},{"key":"ref_48","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Liu, W., Wu, C., Su, H., and Guibas, L.J. (2017). Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv.","DOI":"10.1109\/CVPR.2018.00102"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Ramanan, D., Zitnick, C.L., and Doll\u00e1r, P. (2015). Microsoft COCO: Common Objects in Context. arXiv.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_51","unstructured":"Dwyer, B., Nelson, J., and Solawetz, J. (2024, February 05). Roboflow (Version 1.0). [Software]. Available online: https:\/\/roboflow.com."},{"key":"ref_52","unstructured":"(2024, February 01). Tzutalin. LabelImg. Free Software: MIT License. Available online: https:\/\/github.com\/HumanSignal\/labelImg."},{"key":"ref_53","unstructured":"Dutta, A., Gupta, A., and Zissermann, A. (2024, February 01). VGG Image Annotator (VIA). Version: 2.0.1. Available online: http:\/\/www.robots.ox.ac.uk\/~vgg\/software\/via\/."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.gltp.2022.04.020","article-title":"A review: Data pre-processing and data augmentation techniques","volume":"3","author":"Maharana","year":"2022","journal-title":"Glob. Transit. Proc."},{"key":"ref_55","first-page":"7406","article-title":"Optimised calibration of machine vision system for close range photogrammetry based on machine learning","volume":"34","author":"Vissiere","year":"2022","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.nima.2017.12.065","article-title":"A versatile calibration procedure for portable coded aperture gamma cameras and RGB-D sensors","volume":"886","author":"Paradiso","year":"2018","journal-title":"Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrometer Detect. Assoc. Equip."},{"key":"ref_57","unstructured":"Moreno, C. (2016, January 19\u201321). A Comparative Study of Filtering Methods for Point Clouds in Real-Time Video Streaming. Proceedings of the World Congress on Engineering and Computer Science, San Francisco, CA, USA."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.ifacol.2018.11.566","article-title":"Fast Statistical Outlier Removal Based Method for Large 3D Point Clouds of Outdoor Environments","volume":"51","author":"Balta","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_59","unstructured":"Kingma, D.P., and Ba, J. (2017). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_60","unstructured":"Bertels, J., Eelbode, T., Berman, M., Vandermeulen, D., Maes, F., Bisschops, R., and Blaschko, M.B. (2019). Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2019, Springer International Publishing."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Jena, R., Zhornyak, L., Doiphode, N., Chaudhari, P., Buch, V., Gee, J., and Shi, J. (2023). Beyond mAP: Towards better evaluation of instance segmentation. arXiv.","DOI":"10.1109\/CVPR52729.2023.01088"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1109\/LSP.2021.3084501","article-title":"Generalizing AUC Optimization to Multiclass Classification for Audio Segmentation With Limited Training Data","volume":"28","author":"Gimeno","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_63","unstructured":"Hurtado, J.V., and Valada, A. (2024). Semantic Scene Segmentation for Robotics. arXiv."},{"key":"ref_64","unstructured":"Intel Corporation (2024, January 15). Intel RealSense SDK 2.0\u2013Python Documentation. Developer Documentation. Available online: https:\/\/dev.intelrealsense.com\/docs\/python2."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2889\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:37:52Z","timestamp":1760107072000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,30]]},"references-count":64,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["s24092889"],"URL":"https:\/\/doi.org\/10.3390\/s24092889","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,30]]}}}