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Article: Microplastic pollution monitoring with holographic classification and deep learning
Title | Microplastic pollution monitoring with holographic classification and deep learning |
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Authors | |
Keywords | Deep learning Digital holography Image classification Microplastic pollutant |
Issue Date | 2021 |
Publisher | IOP Publishing: Open Access Journals. The Journal's web site is located at https://iopscience.iop.org/journal/2515-7647 |
Citation | Journal of Physics: Photonics, 2021, v. 3 n. 2, p. article no. 024013 How to Cite? |
Abstract | The observation and detection of the microplastic pollutants generated by industrial manufacturing require the use of precise optical systems. Digital holography is well suited for this task because of its non-contact and non-invasive detection features and the ability to generate information-rich holograms. However, traditional digital holography usually requires post-processing steps, which is time-consuming and may not achieve the final object detection performance. In this work, we develop a deep learning-based holographic classification method, which computes directly on the raw holographic data to extract quantitative information of the microplastic pollutants so as to classify them according to the extent of the pollution. We further show that our method can generalize to the classification task of other micro-objects through cross-dataset validation. Without bulky optical devices, our system can be further developed into a portable microplastics detection system, with wide applicability in the monitoring of microplastic particle pollution in the ecological environment. |
Persistent Identifier | http://hdl.handle.net/10722/304215 |
ISSN | 2023 Impact Factor: 4.6 2023 SCImago Journal Rankings: 1.281 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | ZHU, Y | - |
dc.contributor.author | Yeung, CH | - |
dc.contributor.author | Lam, EY | - |
dc.date.accessioned | 2021-09-23T08:56:51Z | - |
dc.date.available | 2021-09-23T08:56:51Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Journal of Physics: Photonics, 2021, v. 3 n. 2, p. article no. 024013 | - |
dc.identifier.issn | 2515-7647 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304215 | - |
dc.description.abstract | The observation and detection of the microplastic pollutants generated by industrial manufacturing require the use of precise optical systems. Digital holography is well suited for this task because of its non-contact and non-invasive detection features and the ability to generate information-rich holograms. However, traditional digital holography usually requires post-processing steps, which is time-consuming and may not achieve the final object detection performance. In this work, we develop a deep learning-based holographic classification method, which computes directly on the raw holographic data to extract quantitative information of the microplastic pollutants so as to classify them according to the extent of the pollution. We further show that our method can generalize to the classification task of other micro-objects through cross-dataset validation. Without bulky optical devices, our system can be further developed into a portable microplastics detection system, with wide applicability in the monitoring of microplastic particle pollution in the ecological environment. | - |
dc.language | eng | - |
dc.publisher | IOP Publishing: Open Access Journals. The Journal's web site is located at https://iopscience.iop.org/journal/2515-7647 | - |
dc.relation.ispartof | Journal of Physics: Photonics | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Deep learning | - |
dc.subject | Digital holography | - |
dc.subject | Image classification | - |
dc.subject | Microplastic pollutant | - |
dc.title | Microplastic pollution monitoring with holographic classification and deep learning | - |
dc.type | Article | - |
dc.identifier.email | Yeung, CH: chjyeung@HKUCC-COM.hku.hk | - |
dc.identifier.email | Lam, EY: elam@eee.hku.hk | - |
dc.identifier.authority | Yeung, CH=rp02422 | - |
dc.identifier.authority | Lam, EY=rp00131 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1088/2515-7647/abf250 | - |
dc.identifier.scopus | eid_2-s2.0-85104891120 | - |
dc.identifier.hkuros | 324991 | - |
dc.identifier.volume | 3 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | article no. 024013 | - |
dc.identifier.epage | article no. 024013 | - |
dc.identifier.isi | WOS:000641034600001 | - |
dc.publisher.place | United Kingdom | - |