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Computer Vision and Hybrid Reality for Construction Safety Risks: A Pilot Study

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Fourth International Congress on Information and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1027))

Abstract

Construction sites are among the most hazardous venues. While most of the previous research has shed light on the human aspect, we propose to utilise the fast R-CNN object detection method to detect the construction hazard on sites and employ mixed reality to enable the artificial intelligence to detect the hazard. Fast region-based convolutional neural network object detection acquires expert knowledge to identify objects in the image. Unlike image classification, the complexity of object detection always implies an increase in complexity which demands solutions with regard to speed, accuracy and simplicity.

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Acknowledgements

Ocular behaviour, construction hazard awareness and an AI chatbot UGC/FDS15/E01/18.

Willingness to share construction safety knowledge via Web 2.0, mobile apps and IoT, RGC grant, UGC/FDS15/E01/17.

Author information

Authors and Affiliations

  1. Hong Kong Shue Yan University, Hong Kong, China

    Rita Yi Man Li

  2. University of Manchester, Manchester, UK

    Tat Ho Leung

Authors
  1. Rita Yi Man Li
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  2. Tat Ho Leung
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Corresponding author

Correspondence to Rita Yi Man Li .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  2. University of Reading, Reading, UK

    Simon Sherratt

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

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Li, R.Y.M., Leung, T.H. (2020). Computer Vision and Hybrid Reality for Construction Safety Risks: A Pilot Study. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Fourth International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 1027. Springer, Singapore. https://doi.org/10.1007/978-981-32-9343-4_2

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  • DOI: https://doi.org/10.1007/978-981-32-9343-4_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9342-7

  • Online ISBN: 978-981-32-9343-4

  • eBook Packages: EngineeringEngineering (R0)

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