BRADFORD SCHOLARS

    • Sign in
    View Item 
    •   Bradford Scholars
    • Engineering and Informatics
    • Engineering and Informatics Publications
    • View Item
    •   Bradford Scholars
    • Engineering and Informatics
    • Engineering and Informatics Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Bradford ScholarsCommunitiesAuthorsTitlesSubjectsPublication DateThis CollectionAuthorsTitlesSubjectsPublication Date

    My Account

    Sign in

    HELP

    Bradford Scholars FAQsCopyright Fact SheetPolicies Fact SheetDeposit Terms and ConditionsDigital Preservation Policy

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Using deep learning for IoT-enabled smart camera: a use case of flood monitoring

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    View/Open
    Thakker_et_al_DESSERT_2019.pdf (700.6Kb)
    Download
    Publication date
    2019-07
    Author
    Mishra, Bhupesh K.
    Thakker, Dhaval
    Mazumdar, S.
    Simpson, Sydney
    Neagu, Daniel
    Keyword
    Image classification
    Image segmentation
    Deep learning
    DCNN
    IoT sensor
    Drain blockage
    Rights
    © 2019 IEEE. Reproduced in accordance with the publisher's self-archiving policy.
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    In recent years, deep learning has been increasingly used for several applications such as object analysis, feature extraction and image classification. This paper explores the use of deep learning in a flood monitoring application in the context of an EC-funded project, Smart Cities and Open Data REuse (SCORE). IoT sensors for detecting blocked gullies and drainages are notoriously hard to build, hence we propose a novel technique to utilise deep learning for building an IoT-enabled smart camera to address this need. In our work, we apply deep leaning to classify drain blockage images to develop an effective image classification model for different severity of blockages. Using this model, an image can be analysed and classified in number of classes depending upon the context of the image. In building such model, we explored the use of filtering in terms of segmentation as one of the approaches to increase the accuracy of classification by concentrating only into the area of interest within the image. Segmentation is applied in data pre-processing stage in our application before the training. We used crowdsourced publicly available images to train and test our model. Our model with segmentation showed an improvement in the classification accuracy.
    URI
    http://hdl.handle.net/10454/17181
    Version
    Accepted manuscript
    Citation
    Mishra BK, Thakker D, Mazumdar S et al (2019) Using deep learning for IoT-enabled camera: a use case of flood monitoring. In: 10th IEEE International Conference on Dependable Systems, Services and Technologies (DESSERT). 5-7 June 2019, Leeds, United Kingdom. 235-240.
    Link to publisher’s version
    https://doi.org/10.1109/DESSERT.2019.8770019
    Type
    Conference paper
    Collections
    Engineering and Informatics Publications

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.