IDEAS home Printed from https://ideas.repec.org/h/spr/lnopch/978-981-19-5256-2_14.html
   My bibliography  Save this book chapter

A Forewarning Method for Falling Hazard from Hole Based on Instance Segmentation and Regional Invasion Detection

In: Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate

Author

Listed:
  • Rui Wang

    (Tongji University)

  • Yujie Lu

    (Tongji University
    Tongji University
    Tongji University)

  • Shuai Huang

    (Tongji University)

  • Jinshan Liu

    (Tongji University)

  • Mingkang Wang

    (Tongji University)

Abstract

Falls from height (FFH) are a substantial type of accident in the construction industry and cause immense fatal injuries and asset losses. Skills training and hazard awareness training cannot effectively solve the root cause of the fall-related hazard. Implementing automation and informatization with technical intervention to prevent construction hazards has been recognized as a focus topic. However, the recent literature appears to lack scientific management research concerning prevention technology of fall accidents caused by workers reach the hazard zone of the hole. This paper proposes a computer vision-based approach to contribute to the topic of fall hazard prediction and forewarning related to holes in construction sites: (1) instance segmentation module based on Yolact for hole detection and virtual fence generation (2) object detection module based on YOLOv5 for worker detection, and (3) regional invasion detection module for behavior detection. The results show an accuracy rate and a recall rate of behavior detection with value of 69% and 74%, respectively. By detecting the occurrence of workers’ hazard behaviors approaching the hole in real-time, the causal chain of accidents is controlled through forewarning to prevent accidents happen and provide effective assistance for the construction safety administrator.

Suggested Citation

  • Rui Wang & Yujie Lu & Shuai Huang & Jinshan Liu & Mingkang Wang, 2022. "A Forewarning Method for Falling Hazard from Hole Based on Instance Segmentation and Regional Invasion Detection," Lecture Notes in Operations Research, in: Hongling Guo & Dongping Fang & Weisheng Lu & Yi Peng (ed.), Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate, pages 157-174, Springer.
    • Handle: RePEc:spr:lnopch:978-981-19-5256-2_14
    DOI: 10.1007/978-981-19-5256-2_14
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lnopch:978-981-19-5256-2_14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.