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dc.contributor.authorSun, Li
dc.contributor.authorZhao, Cheng
dc.contributor.authorYan, Zhi
dc.contributor.authorLiu, Pengcheng
dc.contributor.authorDuckett, Tom
dc.contributor.authorStolkin, Rustam
dc.date.accessioned2019-01-25T09:49:43Z
dc.date.available2019-01-25T09:49:43Z
dc.date.issued2018-12-19
dc.identifier.citationSun, L., Zhao, C., Yan, Z., Liu, P., Duckett, T. and Stolkin, R., 2018. A Novel Weakly-supervised approach for RGB-D-based Nuclear Waste Object Detection and Categorization. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2018.2888815en_US
dc.identifier.issn1530-437X
dc.identifier.urihttp://hdl.handle.net/10369/10234
dc.descriptionArticle published in IEEE Sensors Journal, available at https://doi.org/10.1109/JSEN.2018.2888815en_US
dc.description.abstractThis paper addresses the problem of RGBD-based detection and categorization of waste objects for nuclear decommissioning. To enable autonomous robotic manipulation for nuclear decommissioning, nuclear waste objects must be detected and categorized. However, as a novel industrial application, large amounts of annotated waste object data are currently unavailable. To overcome this problem, we propose a weakly-supervised learning approach which is able to learn a deep convolutional neural network (DCNN) from unlabelled RGBD videos while requiring very few annotations. The proposed method also has the potential to be applied to other household or industrial applications. We evaluate our approach on the Washington RGBD object recognition benchmark, achieving the state-of-the-art performance among semi-supervised methods. More importantly, we introduce a novel dataset, i.e. Birmingham nuclear waste simulants dataset, and evaluate our proposed approach on this novel industrial object recognition challenge. We further propose a complete real-time pipeline for RGBD-based detection and categorization of nuclear waste simulants. Our weakly-supervised approach has demonstrated to be highly effective in solving a novel RGB-D object detection and recognition application with limited human annotations.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesIEEE Sensors Journal;
dc.titleA Novel Weakly-supervised approach for RGB-D-based Nuclear Waste Object Detection and Categorizationen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/JSEN.2018.2888815
dcterms.dateAccepted2018
rioxxterms.funderCardiff Metropolitan Universityen_US
rioxxterms.identifier.projectCardiff Metropolian (Internal)en_US
rioxxterms.versionAMen_US
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden_US
rioxxterms.licenseref.startdate2019-01-25
rioxxterms.funder.project37baf166-7129-4cd4-b6a1-507454d1372een_US


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