Real-Time Construction Safety Monitoring with Object Detection Algorithms: Features’ Identification and Implementation Challenges

This research bridges this gap by identifying the essential features and functionalities construction professionals prioritize for effective object detection in real-time safety monitoring. By understanding these needs, this study guides the development of more practical and impactful object detecti...

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Veröffentlicht in:Journal of Applied Engineering Sciences Jg. 15; H. 1; S. 135 - 144
Hauptverfasser: Mohy, Amr A., Bassioni, Hesham A., Elgendi, Elbadr O., Hassan, Tarek M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Timișoara Sciendo 01.05.2025
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Schlagworte:
construction safety management
deep learning
features identification
questionnaire Survey
real-time monitoring
safety monitoring
ISSN:2284-7197, 2247-3769, 2284-7197
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Abstract This research bridges this gap by identifying the essential features and functionalities construction professionals prioritize for effective object detection in real-time safety monitoring. By understanding these needs, this study guides the development of more practical and impactful object detection models, ultimately leading to enhanced safety monitoring and management. Through a questionnaire survey targeting 240 construction professionals, this study gathered insights into current safety management practices, practical considerations for technology implementation, and most importantly, the preferred features for object detection systems. The survey revealed an industry trend towards proactive safety management approaches, with a particular emphasis on features that enable real-time worker tracking and work environment monitoring. While acknowledging the potential benefits of object detection technology, professionals also highlighted concerns regarding data privacy, integration with existing systems, and implementation costs. This study addresses these concerns by emphasizing the need for user-focused, affordable, and seamlessly integrated object detection applications.
AbstractList This research bridges this gap by identifying the essential features and functionalities construction professionals prioritize for effective object detection in real-time safety monitoring. By understanding these needs, this study guides the development of more practical and impactful object detection models, ultimately leading to enhanced safety monitoring and management. Through a questionnaire survey targeting 240 construction professionals, this study gathered insights into current safety management practices, practical considerations for technology implementation, and most importantly, the preferred features for object detection systems. The survey revealed an industry trend towards proactive safety management approaches, with a particular emphasis on features that enable real-time worker tracking and work environment monitoring. While acknowledging the potential benefits of object detection technology, professionals also highlighted concerns regarding data privacy, integration with existing systems, and implementation costs. This study addresses these concerns by emphasizing the need for user-focused, affordable, and seamlessly integrated object detection applications.
Author Hassan, Tarek M.
Elgendi, Elbadr O.
Bassioni, Hesham A.
Mohy, Amr A.
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  surname: Mohy
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  email: A.El-Deen5146@student.aast.edu
  organization: Construction and Building Engineering Department, Arab Academy for Science and Technology and Maritime Transport, Egypt
– sequence: 2
  givenname: Hesham A.
  surname: Bassioni
  fullname: Bassioni, Hesham A.
  email: hbassioni@aast.edu
  organization: Construction and Building Engineering Department, Arab Academy for Science and Technology and Maritime Transport, Egypt
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  givenname: Elbadr O.
  surname: Elgendi
  fullname: Elgendi, Elbadr O.
  email: elbadrosman@aast.edu
  organization: Construction and Building Engineering Department, Arab Academy for Science and Technology and Maritime Transport, Egypt
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  givenname: Tarek M.
  surname: Hassan
  fullname: Hassan, Tarek M.
  email: T.Hassan@Lboro.ac.uk
  organization: School of Architecture, Building and Civil Engineering, Loughborough University, United Kingdom
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SubjectTerms construction safety management
deep learning
features identification
questionnaire Survey
real-time monitoring
safety monitoring
Title Real-Time Construction Safety Monitoring with Object Detection Algorithms: Features’ Identification and Implementation Challenges
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