A Novel Weakly-supervised approach for RGB-D-based Nuclear Waste Object Detection and Categorization.pdf (4.96 MB)
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A Novel Weakly-supervised approach for RGB-D-based Nuclear Waste Object Detection and Categorization

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journal contribution
posted on 07.03.2022, 09:13 authored by Li Sun, Cheng Zhao, Zhi Yan, Pengcheng Liu, Tom Duckett, Rustam Stolkin
This 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.

History

Published in

IEEE Sensors Journal

Publisher

IEEE

Version

AM (Accepted Manuscript)

Citation

Sun, 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.2018.2888815

Print ISSN

1530-437X

Electronic ISSN

1558-1748

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Pengcheng Liu

Copyright Holder

© The Publisher

Publisher Rights Statement

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Language

en