Real-time monitoring of work-at-height safety hazards in construction sites using drones and deep learning

Shanti, Mohammad Z.; Cho, Chung-Suk; Soto, Borja García de; Byon, Young-Ji; Yeun, Chan Yeob; Kim, Tae Yeon

doi:10.1016/j.jsr.2022.09.011

Cited by 34 publications

(10 citation statements)

References 8 publications

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“…Fang et al [44] proposed extended Faster R-CNN techniques to recognize non-hardhat users in surveillance videos. Shanti et al [45] proposed a YOLOv4-based technique to detect safety harnesses, lifelines, and helmets. Similarly, refs.…”

Section: Object Detectionmentioning

confidence: 99%

Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

Khan,

Zaidi,

Yang

et al. 2023

Buildings

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Noncompliance with safety rules is a major cause of unsatisfactory performance in construction safety worldwide. Although some research efforts have focused on using computer vision (CV) methods for safety rule inspection, these methods are still in their early stages and cannot be effectively applied on construction job sites. Therefore, it is necessary to present a feasible prototype and conduct a detailed analysis of safety rules to ensure compliance at the job site. This study aims to extend the validation of safety rule analysis through four case scenarios. The proposed structured classification of safety rules includes categorizing them based on project phases and work stages. The construction phase-related rules are divided into four groups: (1) before work, (2) with intervals, (3) during work, and (4) after work. To validate the proposed framework, this research developed prototypes for each group’s scenarios using deep learning algorithms, a storage database to record compliance with safety rules, and an Android application for edge computing, which is required in the “before work” and “after work” groups. The findings of this study could contribute to the development of a compact CV-based safety monitoring system to enhance the current safety management process in the construction industry.

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Section: Object Detectionmentioning

confidence: 99%

Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

Khan,

Zaidi,

Yang

et al. 2023

Buildings

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“…Therefore, we would like to propose an algorithm that guides a wall-climbing cleaning robot to clean thoroughly and timely. This helps to prevent radioactive dust from being generated and reduces the need for dangerous high-altitude work by cleaning staff [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Dust detection and cleanliness assessment based on S-YOLOv5s for NPP reactor containment wall-climbing cleaning robot

Chen,

Zhu,

Zhang

et al. 2024

Heliyon

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“…This capture method is cost-effective and does not require highly trained personnel. Drones are widely used in various applications such as industrial and infrastructure inspections [2,3], agricultural and environmental monitoring [4,5], geographical surveying [6], search and rescue missions [7], security and surveillance [8], and so on.…”

Section: Introductionmentioning

confidence: 99%

Special Issue on Intelligent Image Processing and Sensing for Drones

Yeom

2024

Drones

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Real-time monitoring of work-at-height safety hazards in construction sites using drones and deep learning

Cited by 34 publications

References 8 publications

Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

Construction Work-Stage-Based Rule Compliance Monitoring Framework Using Computer Vision (CV) Technology

Dust detection and cleanliness assessment based on S-YOLOv5s for NPP reactor containment wall-climbing cleaning robot

Special Issue on Intelligent Image Processing and Sensing for Drones

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