International conference on construction engineering and project management (국제학술발표논문집)

  • 2024.07a
  • /
  • Pages.1057-1064
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  • 2024
  • /
  • 2508-9048(eISSN)
Korea Institute of Construction Engineering and Management (한국건설관리학회)
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CV-Based Mobile Application to Enhance Real-time Safety Monitoring of Ladder Activities

  • Muhammad Sibtain Abbas (Department of Architecture Engineering, Chung Ang University) ;
  • Nasrullah Khan (Department of Architecture Engineering, Chung Ang University) ;
  • Syed Farhan Alam Zaidi (Department of Architecture Engineering, Chung Ang University) ;
  • Rahat Hussain (Department of Architecture Engineering, Chung Ang University) ;
  • Aqsa Sabir (Department of Computer Science and Engineering, Chung Ang University) ;
  • Doyeop Lee (Department of Architecture Engineering, Chung Ang University) ;
  • Chansik Park (Department of Architecture Engineering, Chung Ang University)
  • Published : 2024.07.29
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Abstract

The construction industry has witnessed a concerning rise in ladder-related accidents, necessitating the implementation of stricter safety measures. Recent statistics highlight a substantial number of accidents occurring while using ladders, emphasizing the mandatory need for preventative measures. While prior research has explored computer vision-based automatic monitoring for specific aspects such as ladder stability with and without outriggers, worker height, and helmet usage, this study extends existing frameworks by introducing a rule set for co-workers. The research methodology involves training a YOLOv5 model on a comprehensive dataset to detect both the worker on the ladder and the presence of co-workers in real time. The aim is to enable smooth integration of the detector into a mobile application, serving as a portable real-time monitoring tool for safety managers. This mobile application functions as a general safety tool, considering not only conventional risk factors but also ensuring the presence of a co-worker when a worker reaches a specific height. The application offers users an intuitive interface, utilizing the device's camera to identify and verify the presence of coworkers during ladder activities. By combining computer vision technology with mobile applications, this study presents an innovative approach to ladder safety that prioritizes real-time, on-site co-worker verification, thereby significantly reducing the risk of accidents in construction environments. With an overall mean average precision (mAP) of 97.5 percent, the trained model demonstrates its effectiveness in detecting unsafe worker behavior within a construction environment.

Keywords

Acknowledgement

This research was conducted with the support of the "National R&D Project for Smart Construction Technology (No.RS-2020-KA156291)" funded by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport, and managed by the Korea Expressway Corporation.

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