Automated Methods for Activity Recognition of Construction Workers and Equipment: State-of-the-Art Review

Sherafat, Behnam; Ahn, Changbum R.; Akhavian, Reza; Behzadan, Amir H.; Golparvar-Fard, Mani; Kim, Hyunsoo; Lee, Yong-Cheol; Rashidi, Abbas; Azar, Ehsan Rezazadeh

doi:10.1061/(asce)co.1943-7862.0001843

Cited by 146 publications

(86 citation statements)

References 113 publications

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“…We review some antecedents already in the literature regarding the use of AI in the AEC industry. We summarize the results from few reviews directly addressing the AEC and AI topic [1]- [4], [9]- [11], [13].…”

Section: Ai and Aec Industrymentioning

confidence: 99%

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Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis

Maureira¹,

Pinto

Yepes³

et al. 2021

IEEE Access

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The Architecture, Engineering, and Construction (AEC) Industry is one of the most important productive sectors, hence also produce a high impact on the economic balances, societal stability, and global challenges in climate change. Regarding its adoption of technologies, applications and processes is also recognized by its status-quo, its slow innovation pace, and the conservative approaches. However, a new technological era -Industry 4.0 fueled by AI-is driving productive sectors in a highly pressurized global technological competition and sociopolitical landscape. In this paper, we develop an adaptive approach to mining text content in the literature research corpus related to the AEC and AI (AEC-AI) industries, in particular on its relation to technological processes and applications. We present a first stage approach to an adaptive assessment of AI algorithms, to form an integrative AI platform in the AEC industry, the AEC-AI industry 4.0. At this stage, a macroscopic adaptive method is deployed to characterize "Optimization", a key term in AEC-AI industry, using a mixed methodology incorporating machine learning and classical evaluation process. Our results show that effective use of metadata, constrained search queries, and domain knowledge allows getting a macroscopic assessment of the target concept. This allows the extraction of a high-level mapping and conceptual structure characterization of the literature corpus. The results are comparable, at this level, to classical methodologies for the literature review. In addition, our method is designed for an adaptive assessment to incorporate further stages.

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Section: Ai and Aec Industrymentioning

confidence: 99%

“…The conceptual structure map using the CA method shows a broad spanning axis from optimization, ontology, knowledge up to geopolym, slag, carbon, mortar forming a construction material method axis (9). The orthogonal direction with less variance appears to display methods in ML as optimization, SVM, firefly, hybrid.…”

Section: Reviewsmentioning

confidence: 99%

Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis

Maureira¹,

Pinto

Yepes³

et al. 2021

IEEE Access

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“…The vision-based approach is applied to activity detection and tracking of construction workers. Such as, detecting near-miss incident, unsafe worker motions and assigning specific tasks to workers [9]. Helmet detection and Human Identity…”

Section: Introductionmentioning

confidence: 99%

Worker’s Helmet Recognition and Identity Recognition Based on Deep Learning

Wang¹,

Zhu²,

Wu³

et al. 2021

OJMSi

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For decades, safety has been a concern for the construction industry. Helmet detection caught the attention of machine learning, but the problem of identity recognition has been ignored in previous studies, which brings trouble to the subsequent safety education of workers. Although, many scholars have devoted themselves to the study of person re-identification which neglected safety detection. The study of this paper mainly proposes a method based on deep learning, which is different from the previous study of helmet detection and human identity recognition and can carry out helmet detection and identity recognition for construction workers. This paper proposes a computer vision-based worker identity recognition and helmet recognition method. We collected 3000 real-name channel images and constructed a neural network based on the You Only Look Once (YOLO) v3 model to extract the features of the construction worker's face and helmet, respectively. Experiments show that the method has a high recognition accuracy rate, fast recognition speed, accurate recognition of workers and helmet detection, and solves the problem of poor supervision of real-name channels.

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“…Despite their encouraging findings, it remains a major challenge to exploit computer vision technologies in real-world construction sites owing to various theoretical and practical issues, implying that there are knowledge gaps in practice and theory. Hence, there have been a few studies to review computer vision research in construction and expand our understanding on state-of-the-art technologies (Seo et al, 2015a;Teizer, 2015;Yang et al, 2015;Ham et al, 2016;Xiao and Zhu, 2018;Fang et al, 2020b,c;Sherafat et al, 2020;Zhang et al, 2020b). However, because some of the review articles (Seo et al, 2015a;Teizer, 2015;Yang et al, 2015;Ham et al, 2016) were published before 2016, recent advances in deep learning and computer vision algorithms could not be covered.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, Fang et al (2020b; and Zhang et al (2020b) investigated existing studies in the perspective of safety monitoring. The reviews of Xiao and Zhu (2018) and Sherafat et al (2020) were limited only to construction object tracking and action recognition technologies, respectively. Thus, knowledge gaps still remain unclear over the various purposes and technologies of operationlevel monitoring and documentation.…”

Section: Introductionmentioning

confidence: 99%

Visual Analytics for Operation-Level Construction Monitoring and Documentation: State-of-the-Art Technologies, Research Challenges, and Future Directions

Kim

2020

Front. Built Environ.

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Operation-level vision-based monitoring and documentation has drawn significant attention from construction practitioners and researchers. To automate the operation-level monitoring of construction and built environments, there have been much effort to develop computer vision technologies. Despite their encouraging findings, it remains a major challenge to exploit technologies in real construction projects, implying that there are knowledge gaps in practice and theory. To fill such knowledge gaps, this study thoroughly reviews 119 papers on operation-level vision-based construction monitoring, published in mainstream construction informatics journals. Existing research papers can be categorized into three sequential technologies: (1) camera placement for operation-level construction monitoring, (2) single-camera-based construction monitoring and documentation, and (3) multi-camera-based onsite information integration and construction monitoring. For each technology, state-of-the-art algorithms, open challenges, and future directions are discussed.

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Automated Methods for Activity Recognition of Construction Workers and Equipment: State-of-the-Art Review

Cited by 146 publications

References 113 publications

Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis

Towards an AEC-AI Industry Optimization Algorithmic Knowledge Mapping: An Adaptive Methodology for Macroscopic Conceptual Analysis

Worker’s Helmet Recognition and Identity Recognition Based on Deep Learning

Visual Analytics for Operation-Level Construction Monitoring and Documentation: State-of-the-Art Technologies, Research Challenges, and Future Directions

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