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Conference Paper: Digital holography with deep learning and generative adversarial networks for automatic microplastics classification

TitleDigital holography with deep learning and generative adversarial networks for automatic microplastics classification
Authors
Issue Date2020
PublisherSPIE - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml?WT.svl=mddp2
Citation
SPIE Conference: SPIE/COS Photonics Asia: Holography, Diffractive Optics, and Applications X, Online Meeting, China, 11-16 October 2020. In Sheng, Y... (et al) (eds.), Proceedings of SPIE, v. 11511, Paper 115510A How to Cite?
AbstractMicroplastics, which are a major source of pollution in the ocean, need to be accurately detected and monitored. However, the current detection approaches often require complex optical instrumentation and a long time for image processing. Furthermore, because of the difficulties of particle sampling, it is hard to collect a dataset with sufficient images and a balanced distribution. Digital holography, which is a non-destructive imaging method, is suitable for the in situ imaging. In this work, we propose a novel digital holography microplastics classification system which combines deep learning and generative adversarial networks. We experimentally show that our method yields a higher accuracy for microplastics classification and can efficiently reduce the imbalance ratio of the dataset. This method can be modified for other in situ image classification tasks that likewise suffer from a small and imbalanced distribution dataset.
Persistent Identifierhttp://hdl.handle.net/10722/290716
ISBN

 

DC FieldValueLanguage
dc.contributor.authorZHU, Y-
dc.contributor.authorYeung, CH-
dc.contributor.authorLam, EYM-
dc.date.accessioned2020-11-02T05:46:06Z-
dc.date.available2020-11-02T05:46:06Z-
dc.date.issued2020-
dc.identifier.citationSPIE Conference: SPIE/COS Photonics Asia: Holography, Diffractive Optics, and Applications X, Online Meeting, China, 11-16 October 2020. In Sheng, Y... (et al) (eds.), Proceedings of SPIE, v. 11511, Paper 115510A-
dc.identifier.isbn9781510639171-
dc.identifier.urihttp://hdl.handle.net/10722/290716-
dc.description.abstractMicroplastics, which are a major source of pollution in the ocean, need to be accurately detected and monitored. However, the current detection approaches often require complex optical instrumentation and a long time for image processing. Furthermore, because of the difficulties of particle sampling, it is hard to collect a dataset with sufficient images and a balanced distribution. Digital holography, which is a non-destructive imaging method, is suitable for the in situ imaging. In this work, we propose a novel digital holography microplastics classification system which combines deep learning and generative adversarial networks. We experimentally show that our method yields a higher accuracy for microplastics classification and can efficiently reduce the imbalance ratio of the dataset. This method can be modified for other in situ image classification tasks that likewise suffer from a small and imbalanced distribution dataset.-
dc.languageeng-
dc.publisherSPIE - International Society for Optical Engineering. The Journal's web site is located at http://spie.org/x1848.xml?WT.svl=mddp2-
dc.relation.ispartofProceedings of SPIE, v. 11511: Holography, Diffractive Optics, and Applications X-
dc.rights© SPIE - International Society for Optical Engineering.-
dc.titleDigital holography with deep learning and generative adversarial networks for automatic microplastics classification-
dc.typeConference_Paper-
dc.identifier.emailYeung, CH: chjyeung@hku.hk-
dc.identifier.emailLam, EYM: elam@eee.hku.hk-
dc.identifier.authorityYeung, CH=rp02422-
dc.identifier.authorityLam, EYM=rp00131-
dc.identifier.doi10.1117/12.2575115-
dc.identifier.hkuros318389-
dc.identifier.volume11511-
dc.publisher.placeUnited States-

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