{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T12:53:24Z","timestamp":1772196804884,"version":"3.50.1"},"reference-count":37,"publisher":"IOP Publishing","issue":"2","license":[{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"tdm","delay-in-days":12,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62175059"],"award-info":[{"award-number":["62175059"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Research and Development Project of Handan City","award":["19422083008-69"],"award-info":[{"award-number":["19422083008-69"]}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"crossref","award":["F2018402285"],"award-info":[{"award-number":["F2018402285"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Project of Hebei Education Department","award":["QN2020426"],"award-info":[{"award-number":["QN2020426"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Accurate three-dimensional positioning of particles is a critical task in microscopic particle research, with one of the main challenges being the measurement of particle depths. In this paper, we propose a method for detecting particle depths from their blurred images using the depth-from-defocus technique and a deep neural network-based object detection framework called you-only-look-once. Our method provides simultaneous lateral position information for the particles and has been tested and evaluated on various samples, including synthetic particles, polystyrene particles, blood cells, and plankton, even in a noise-filled environment. We achieved autofocus for target particles in different depths using generative adversarial networks, obtaining clear-focused images. Our algorithm can process a single multi-target image in 0.008 s, allowing real-time application. Our proposed method provides new opportunities for particle field research.<\/jats:p>","DOI":"10.1088\/2632-2153\/acdb2e","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T22:56:57Z","timestamp":1685746617000},"page":"025030","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["3D positioning and autofocus of the particle field based on the depth-from-defocus method and the deep networks"],"prefix":"10.1088","volume":"4","author":[{"given":"Xiaolei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5849-1979","authenticated-orcid":true,"given":"Zhao","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Sha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengsheng","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaokai","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"key":"mlstacdb2ebib1","doi-asserted-by":"publisher","DOI":"10.1038\/lsa.2017.46","article-title":"Air quality monitoring using mobile microscopy and machine learning","volume":"6","author":"Wu","year":"2017","journal-title":"Light-Sci. Appl."},{"key":"mlstacdb2ebib2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-06311-y","article-title":"Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning","volume":"7","author":"Yoon","year":"2017","journal-title":"Sci. Rep."},{"key":"mlstacdb2ebib3","doi-asserted-by":"publisher","first-page":"4275","DOI":"10.1021\/acs.cgd.8b00883","article-title":"Image analysis for in-line measurement of multidimensional size, shape, and polymorphic transformation of l-glutamic acid using deep learning-based image segmentation and classification","volume":"18","author":"Gao","year":"2018","journal-title":"Cryst. Growth Des."},{"key":"mlstacdb2ebib4","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ac8892","article-title":"Deep learning based instance segmentation of particle streaks and tufts","volume":"33","author":"Tsalicoglou","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"mlstacdb2ebib5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12650-011-0107-9","article-title":"Particle imaging techniques for volumetric three-component (3D3C) velocity measurements in microfluidics","volume":"15","author":"Cierpka","year":"2012","journal-title":"J. Visual-Japan"},{"key":"mlstacdb2ebib6","doi-asserted-by":"publisher","first-page":"9134","DOI":"10.1364\/OE.22.009134","article-title":"Precise calibration of binocular vision system used for vision measurement","volume":"22","author":"Cui","year":"2014","journal-title":"Opt. Express"},{"key":"mlstacdb2ebib7","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2022.126427","article-title":"An improved computation method for asphalt pavement texture depth based on multiocular vision 3D reconstruction technology","volume":"321","author":"Dan","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"mlstacdb2ebib8","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1109\/TCI.2021.3063870","article-title":"Holographic 3D particle imaging with model-based deep network","volume":"7","author":"Chen","year":"2021","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"mlstacdb2ebib9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00348-019-2818-y","article-title":"Deep learning-based accurate and rapid tracking of 3D positional information of microparticles using digital holographic microscopy","volume":"60","author":"Lee","year":"2019","journal-title":"Exp. Fluids"},{"key":"mlstacdb2ebib10","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1109\/TPAMI.1987.4767940","article-title":"A new sense for depth of field","volume":"4","author":"Pentland","year":"1987","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"mlstacdb2ebib11","doi-asserted-by":"publisher","DOI":"10.3788\/COL201614.031201","article-title":"Three-dimensional positioning method for moving particles based on defocused imaging using single-lens dual-camera system","volume":"14","author":"Zhou","year":"2016","journal-title":"Chin Opt. Lett."},{"key":"mlstacdb2ebib12","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/abfef6","article-title":"Defocus particle tracking: a comparison of methods based on model functions, cross-correlation, and neural networks","volume":"32","author":"Barnkob","year":"2021","journal-title":"Meas. Sci. Technol."},{"key":"mlstacdb2ebib13","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/abad71","article-title":"A fast and robust algorithm for general defocusing particle tracking","volume":"32","author":"Rossi","year":"2020","journal-title":"Meas. Sci. Technol."},{"key":"mlstacdb2ebib14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00348-020-02968-w","article-title":"Cut, overlap and locate: a deep learning approach for the 3D localization of particles in astigmatic optical setups","volume":"61","author":"Franchini","year":"2020","journal-title":"Exp. Fluids"},{"key":"mlstacdb2ebib15","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1038\/s41592-020-0853-5","article-title":"DeepSTORM3D: dense 3D localization microscopy and PSF design by deep learning","volume":"17","author":"Nehme","year":"2020","journal-title":"Nat. Methods"},{"key":"mlstacdb2ebib16","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ac8a09","article-title":"Particle detection by means of neural networks and synthetic training data refinement in defocusing particle tracking velocimetry","volume":"33","author":"Dreisbach","year":"2022","journal-title":"Meas. Sci. Technol."},{"key":"mlstacdb2ebib17","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s00348-023-03574-2","article-title":"Particle detection and size recognition based on defocused particle images: a comparison of a deterministic algorithm and a deep neural network","volume":"64","author":"Sachs","year":"2023","journal-title":"Exp. Fluids"},{"key":"mlstacdb2ebib18","doi-asserted-by":"publisher","first-page":"8843","DOI":"10.1364\/AO.471105","article-title":"Learning local depth regression from defocus blur by soft-assignment encoding","volume":"61","author":"Leroy","year":"2022","journal-title":"Appl. Opt."},{"key":"mlstacdb2ebib19","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2022.106989","article-title":"Particle field positioning with a commercial microscope based on a developed CNN and the depth-from-defocus method","volume":"153","author":"Zhang","year":"2022","journal-title":"Opt. Lasers Eng."},{"key":"mlstacdb2ebib20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106780","article-title":"An improved YOLOv5 model based on visual attention mechanism: application to recognition of tomato virus disease","volume":"194","author":"Qi","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"mlstacdb2ebib21","doi-asserted-by":"publisher","first-page":"310","DOI":"10.3390\/jmse10030310","article-title":"Underwater target detection algorithm based on improved YOLOv5","volume":"10","author":"Lei","year":"2022","journal-title":"J. Mar. Sci. Eng."},{"key":"mlstacdb2ebib22","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1109\/34.709612","article-title":"Selecting the optimal focus measure for autofocusing and depth-from-focus","volume":"20","author":"Subbarao","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"mlstacdb2ebib23","doi-asserted-by":"publisher","first-page":"4315","DOI":"10.1109\/ICIP.2019.8803419","article-title":"Learning optimal phase-coded aperture for depth of field extension","author":"Akpinar","year":"2019"},{"key":"mlstacdb2ebib24","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1364\/OPTICA.5.000704","article-title":"Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery","volume":"5","author":"Wu","year":"2018","journal-title":"Optica"},{"key":"mlstacdb2ebib25","doi-asserted-by":"publisher","first-page":"2242","DOI":"10.1109\/ICCV.2017.244","article-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","author":"Zhu","year":"2017"},{"key":"mlstacdb2ebib26","doi-asserted-by":"publisher","first-page":"771","DOI":"10.3390\/electronics10070771","article-title":"Improved YOLOV3 network for insulator detection in aerial images with diverse background interference","volume":"10","author":"Liu","year":"2021","journal-title":"Electronics"},{"key":"mlstacdb2ebib27","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/978-3-030-72073-5_3","volume":"vol 1386","author":"Wang","year":"and 2021","edition":"ed"},{"key":"mlstacdb2ebib28","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.3390\/rs13091619","article-title":"A real-time apple targets detection method for picking robot based on improved YOLOv5","volume":"13","author":"Yan","year":"2021","journal-title":"Remote Sens."},{"key":"mlstacdb2ebib29","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.3390\/electronics10141711","article-title":"A real-time detection algorithm for kiwifruit defects based on YOLOv5","volume":"10","author":"Yao","year":"2021","journal-title":"Electronics"},{"key":"mlstacdb2ebib30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105742","article-title":"Using channel pruning-based YOLOv4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments","volume":"178","author":"Wu","year":"2020","journal-title":"Comput. Electron. Agr."},{"key":"mlstacdb2ebib31","doi-asserted-by":"publisher","DOI":"10.1016\/j.optcom.2021.127454","article-title":"Resolution enhancement in microscopic imaging based on generative adversarial network with unpaired data","volume":"503","author":"Wang","year":"2021","journal-title":"Opt. Commun."},{"key":"mlstacdb2ebib32","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1016\/j.procs.2018.07.112","article-title":"YOLO based human action recognition and localization","volume":"133","author":"Shinde","year":"2018","journal-title":"Proc. Comput. Sci."},{"key":"mlstacdb2ebib33","doi-asserted-by":"publisher","first-page":"4758","DOI":"10.3390\/app11114758","article-title":"Augmented reality maintenance assistant using YOLOv5","volume":"11","author":"Malta","year":"2021","journal-title":"Appl. Sci."},{"key":"mlstacdb2ebib34","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/ab42bb","article-title":"Synthetic image generator for defocusing and astigmatic PIV\/PTV","volume":"31","author":"Rossi","year":"2019","journal-title":"Meas. Sci. Technol."},{"key":"mlstacdb2ebib35","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abc12f","article-title":"Generative adversarial network-based sinogram super-resolution for computed tomography imaging","volume":"65","author":"Tang","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"mlstacdb2ebib36","doi-asserted-by":"publisher","first-page":"5967","DOI":"10.1109\/CVPR.2017.632.","article-title":"Image-to-image translation with conditional adversarial networks","author":"Isola","year":"2017"},{"key":"mlstacdb2ebib37","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/acdb2e","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/acdb2e\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/acdb2e","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/acdb2e\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/acdb2e\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/acdb2e\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/acdb2e\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/acdb2e\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T06:58:52Z","timestamp":1686639532000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/acdb2e"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":37,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,6,13]]},"published-print":{"date-parts":[[2023,6,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/acdb2e","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,1]]},"assertion":[{"value":"3D positioning and autofocus of the particle field based on the depth-from-defocus method and the deep networks","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2023 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2022-09-03","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-06-02","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-06-13","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}