A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Computer vision applications in construction safety assurance
Abstract Advancements in the development of deep learning and computer vision-based approaches have the potential to provide managers and engineers with the ability to improve the safety performance of their construction operations on-site. In practice, however, the application of deep learning and computer vision has been limited due to an array of technical (e.g., accuracy and reliability) and managerial challenges. These challenges are a product of the dynamic and complex nature of construction and the difficulties associated with acquiring video surveillance data. In this paper, we design and develop a deep learning and computer vision-based framework for safety in construction by integrating an array of digital technologies with multiple aspects of data fusion. Then, we review existing studies that have focused on identifying unsafe behavior and work conditions and develop a computer-vision enabled framework that: (1) considers current progress on computer vision and deep learning for safety; (2) identifies the research challenges that can materialize with using deep learning to identify unsafe behavior and work conditions; and (3) can provide a signpost for future research in the emergent and fertile area of deep-learning within the context of safety.
Graphical abstract Integration of safety, data fusion and digital technologies. In this research, a framework is designed and developed to enable computer vision to be used to monitor safety by integrating digital technologies and multiple aspects of data fusion as shown in . Then, we review existing studies that have focused on identifying unsafe behavior and work conditions in accordance with our developed framework by placing emphasis on: (1) the existing state-of-the-art methods and their current progress; (2) identifying the research challenges that can materialize with using deep learning to identify unsafe behavior and work conditions; and (3) providing a signpost for future research in the emergent and fertile area of deep-learning within the context of safety. Display Omitted
Highlights An innovative computer vision-based framework for safety in construction is developed Studies that have focused on identifying unsafe behaviour in accordance with our developed framework are reviewed. A signpost for future research in the area of deep-learning within the context of safety is provided.
Computer vision applications in construction safety assurance
Abstract Advancements in the development of deep learning and computer vision-based approaches have the potential to provide managers and engineers with the ability to improve the safety performance of their construction operations on-site. In practice, however, the application of deep learning and computer vision has been limited due to an array of technical (e.g., accuracy and reliability) and managerial challenges. These challenges are a product of the dynamic and complex nature of construction and the difficulties associated with acquiring video surveillance data. In this paper, we design and develop a deep learning and computer vision-based framework for safety in construction by integrating an array of digital technologies with multiple aspects of data fusion. Then, we review existing studies that have focused on identifying unsafe behavior and work conditions and develop a computer-vision enabled framework that: (1) considers current progress on computer vision and deep learning for safety; (2) identifies the research challenges that can materialize with using deep learning to identify unsafe behavior and work conditions; and (3) can provide a signpost for future research in the emergent and fertile area of deep-learning within the context of safety.
Graphical abstract Integration of safety, data fusion and digital technologies. In this research, a framework is designed and developed to enable computer vision to be used to monitor safety by integrating digital technologies and multiple aspects of data fusion as shown in . Then, we review existing studies that have focused on identifying unsafe behavior and work conditions in accordance with our developed framework by placing emphasis on: (1) the existing state-of-the-art methods and their current progress; (2) identifying the research challenges that can materialize with using deep learning to identify unsafe behavior and work conditions; and (3) providing a signpost for future research in the emergent and fertile area of deep-learning within the context of safety. Display Omitted
Highlights An innovative computer vision-based framework for safety in construction is developed Studies that have focused on identifying unsafe behaviour in accordance with our developed framework are reviewed. A signpost for future research in the area of deep-learning within the context of safety is provided.
Computer vision applications in construction safety assurance
Fang, Weili (author) / Ding, Lieyun (author) / Love, Peter E.D. (author) / Luo, Hanbin (author) / Li, Heng (author) / Peña-Mora, Feniosky (author) / Zhong, Botao (author) / Zhou, Cheng (author)
2019-11-12
Article (Journal)
Electronic Resource
English
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