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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Vydáno v:Journal of Applied Engineering Sciences Ročník 15; číslo 1; s. 135 - 144
Hlavní autoři: Mohy, Amr A., Bassioni, Hesham A., Elgendi, Elbadr O., Hassan, Tarek M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Timișoara Sciendo 01.05.2025
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Témata:
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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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2284-7197
2247-3769
2284-7197
DOI:10.2478/jaes-2025-0017