IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i23p16312-d994507.html
   My bibliography  Save this article

Smart Helmet-Based Proximity Warning System to Improve Occupational Safety on the Road Using Image Sensor and Artificial Intelligence

Author

Listed:
  • Yeanjae Kim

    (Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Republic of Korea)

  • Yosoon Choi

    (Department of Energy Resources Engineering, Pukyong National University, Busan 48513, Republic of Korea)

Abstract

Recently, collisions between equipment and workers occur frequently on the road in construction and surface mining sites. To prevent such accidents, we developed a smart helmet-based proximity warning system (PWS) that facilitates visual and tactile proximity warnings. In this system, a smart helmet comprising an Arduino Uno board and a camera module with built-in Wi-Fi was used to transmit images captured by the camera to a smartphone via Wi-Fi. When the image was analyzed through object detection and a heavy-duty truck or other vehicle was detected in an image, the smartphone transmitted a signal to the Arduino via Bluetooth and, when a signal was received, an LED strip with a three-color LED visually alerted the worker and the equipment operator. The performance of the system tested the recognition distance of the helmet according to the pixel size of the detected image in an outdoor environment. The proposed personal PWS can directly produce visual proximity warnings to both workers and operators enabling them to quickly identify and evacuate from dangerous situations.

Suggested Citation

  • Yeanjae Kim & Yosoon Choi, 2022. "Smart Helmet-Based Proximity Warning System to Improve Occupational Safety on the Road Using Image Sensor and Artificial Intelligence," IJERPH, MDPI, vol. 19(23), pages 1-15, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16312-:d:994507
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/23/16312/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/23/16312/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jieun Baek & Yosoon Choi, 2020. "Smart Glasses-Based Personnel Proximity Warning System for Improving Pedestrian Safety in Construction and Mining Sites," IJERPH, MDPI, vol. 17(4), pages 1-15, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel Salinas & Felipe Muñoz-La Rivera & Javier Mora-Serrano, 2022. "Critical Analysis of the Evaluation Methods of Extended Reality (XR) Experiences for Construction Safety," IJERPH, MDPI, vol. 19(22), pages 1-26, November.
    2. Zheng Zhu & Jingfeng Yuan & Qiuhu Shao & Lei Zhang & Guangqi Wang & Xuewei Li, 2020. "Developing Key Safety Management Factors for Construction Projects in China: A Resilience Perspective," IJERPH, MDPI, vol. 17(17), pages 1-20, August.

    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:gam:jijerp:v:19:y:2022:i:23:p:16312-:d:994507. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.