A Vision-Based Collision Monitoring System for Proximity of Construction Workers to Trucks Enhanced by Posture-Dependent Perception and Truck Bodies’ Occupied Space

Shin, Yoonsoo; Kim, Junhee

doi:10.3390/su14137934

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…Yan et al [35] employed the Faster R-CNN algorithm to detect key points of construction trucks, followed by 3D reconstruction to establish distance recognition in the 3D scene. Shin et al [146] integrated YOLO v3 and the Openpose algorithm to identify collision risks between construction workers and trucks. Zhang and Ge [77] employed the latest Transformer technology to identify dynamic collision risks between construction workers and tower cranes.…”

Section: Computer Vision Techniques Facilitated In Extracting Postura...mentioning

confidence: 99%

A Review of Computer Vision-Based Monitoring Approaches for Construction Workers’ Work-Related Behaviors

Li,

Miao,

Zou

et al. 2024

IEEE Access

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Construction workers' behaviors directly affects labor productivity and their own safety, thereby influencing project quality. Recognizing and monitoring the construction-related behaviors is therefore crucial for high-quality management and orderly construction site operation. Recent strides in computer vision technology suggest its potential to replace traditional manual supervision approaches. This paper explores research on monitoring construction workers' behaviors using computer vision. Through bibliometrics and content-based analysis, the authors present the latest research in this area from three perspectives: "Detection, Localization, and Tracking for Construction Workers," "Recognition of Workers' Construction Activities," and "Occupational Health and Safety Behavior Monitoring." In terms of the literature's volume, there has been a notable increase in this field. Notably, the focus on safety-related literature is predominant, underscoring the concern for occupational health. Vision algorithms have witnessed an increase in the utilization of object detection. The ongoing and future research trajectory is anticipated to involve multi-algorithm integration and an emphasis on enhancing robustness. Then the authors summarize the review from engineering impact and technical suitability, and analyze the limitations of current research from the perspectives of technical approaches and application scenarios. Finally, it discusses future research directions in this field together with generative AI models. Furthermore, the authors hope this paper can serves as a valuable reference for both scholars and engineers.

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Section: Computer Vision Techniques Facilitated In Extracting Postura...mentioning

confidence: 99%

A Review of Computer Vision-Based Monitoring Approaches for Construction Workers’ Work-Related Behaviors

Li,

Miao,

Zou

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Instead of falling from a height, researchers also worked on other accidents, such as struck-by and electrocution accidents. Shin et al [54] trained the YOLOv3 model to detect workers and trucks and calculated the collision proximity to avoid struck-by accidents. Li et al [55] detected person, PPEs, and electric work equipment, and calculated the safe distance between person/worker and electric equipment.…”

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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Using Eye-Tracking to Measure Worker Situation Awareness in Augmented Reality

Wu,

Chen,

Hou

et al. 2024

Automation in Construction

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A Vision-Based Collision Monitoring System for Proximity of Construction Workers to Trucks Enhanced by Posture-Dependent Perception and Truck Bodies’ Occupied Space

Cited by 7 publications

References 40 publications

A Review of Computer Vision-Based Monitoring Approaches for Construction Workers’ Work-Related Behaviors

A Review of Computer Vision-Based Monitoring Approaches for Construction Workers’ Work-Related Behaviors

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

Using Eye-Tracking to Measure Worker Situation Awareness in Augmented Reality

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