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    A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory

    Access Status
    Fulltext not available
    Authors
    Ding, L.
    Fang, W.
    Luo, H.
    Love, Peter
    Zhong, B.
    Ouyang, X.
    Date
    2018
    Type
    Journal Article
    
    Metadata
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    Citation
    Ding, L. and Fang, W. and Luo, H. and Love, P. and Zhong, B. and Ouyang, X. 2018. A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory. Automation in Construction. 86: pp. 118-124.
    Source Title
    Automation in Construction
    DOI
    10.1016/j.autcon.2017.11.002
    ISSN
    0926-5805
    School
    Department of Civil Engineering
    URI
    http://hdl.handle.net/20.500.11937/59296
    Collection
    • Curtin Research Publications
    Abstract

    Computer vision and pattern recognition approaches have been applied to determine unsafe behaviors on construction sites. Such approaches have been reliant on the computation of artificially complex image features that utilize a cumbersome parameter re-adjustment process. The creation of image features that can recognize unsafe actions, however, poses a significant research challenge on construction sites. This due to the prevailing complexity of spatio-temporal features, lighting, and the array of viewpoints that are required to identify an unsafe action. Considering these challenges, a new hybrid deep learning model that integrates a convolution neural network (CNN) and long short-term memory (LSTM) that automatically recognizes workers' unsafe actions is developed. The proposed hybrid deep learning model is used to: (1) identify unsafe actions; (2) collect motion data and site videos; (3) extract the visual features from videos using a CNN model; and (4) sequence the learning features that are enabled by the use of LSTM models. An experiment is used to test the model's ability to detect unsafe actions. The results reveal that the developed hybrid model (CNN + LSTM) is able to accurately detect safe/unsafe actions conducted by workers on-site. The model's accuracy exceeds the current state-of-the-art descriptor-based methods for detecting points of interest on images.

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