A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Enhancing Worker Safety: Real-Time Automated Detection of Personal Protective Equipment to Prevent Falls from Heights at Construction Sites Using Improved YOLOv8 and Edge Devices
https://orcid.org/0000-0003-1549-515X
Personal protective equipment (PPE), including helmets, harnesses, and lanyards, is pivotal in preventing falls from heights at construction sites. However, ensuring consistent and correct usage of PPE presents a significant challenge. To address this issue, this study introduces an enhanced You Only Look Once, version 8 model (YOLOv8), a computer-vision-based AI model tailored for real-time multiclass PPE monitoring on portable edge devices. A pioneering large-scale multiclass PPE data set is curated to facilitate model training. Balancing detection accuracy with a lightweight design, we augment YOLOv8 through the integration of the coordinate attention module, ghost convolution module, transfer learning, and merge-nonmaximum suppression. The proposed model surpasses the original YOLOv8 and state-of-the-art models, showcasing improved accuracy and reduced computational cost. Deployed on the edge device Jetson Xavier NX, the model achieves precise PPE detection (: 92.52%) in real-time, operating at 9.11 frames per second. These findings establish a robust foundation for the efficient and real-time automated safety monitoring of construction sites, promising substantial enhancements to worker safety and data privacy within the construction industry.
Enhancing Worker Safety: Real-Time Automated Detection of Personal Protective Equipment to Prevent Falls from Heights at Construction Sites Using Improved YOLOv8 and Edge Devices
https://orcid.org/0000-0003-1549-515X
Personal protective equipment (PPE), including helmets, harnesses, and lanyards, is pivotal in preventing falls from heights at construction sites. However, ensuring consistent and correct usage of PPE presents a significant challenge. To address this issue, this study introduces an enhanced You Only Look Once, version 8 model (YOLOv8), a computer-vision-based AI model tailored for real-time multiclass PPE monitoring on portable edge devices. A pioneering large-scale multiclass PPE data set is curated to facilitate model training. Balancing detection accuracy with a lightweight design, we augment YOLOv8 through the integration of the coordinate attention module, ghost convolution module, transfer learning, and merge-nonmaximum suppression. The proposed model surpasses the original YOLOv8 and state-of-the-art models, showcasing improved accuracy and reduced computational cost. Deployed on the edge device Jetson Xavier NX, the model achieves precise PPE detection (: 92.52%) in real-time, operating at 9.11 frames per second. These findings establish a robust foundation for the efficient and real-time automated safety monitoring of construction sites, promising substantial enhancements to worker safety and data privacy within the construction industry.
Enhancing Worker Safety: Real-Time Automated Detection of Personal Protective Equipment to Prevent Falls from Heights at Construction Sites Using Improved YOLOv8 and Edge Devices
https://orcid.org/0000-0003-1549-515X
Personal protective equipment (PPE), including helmets, harnesses, and lanyards, is pivotal in preventing falls from heights at construction sites. However, ensuring consistent and correct usage of PPE presents a significant challenge. To address this issue, this study introduces an enhanced You Only Look Once, version 8 model (YOLOv8), a computer-vision-based AI model tailored for real-time multiclass PPE monitoring on portable edge devices. A pioneering large-scale multiclass PPE data set is curated to facilitate model training. Balancing detection accuracy with a lightweight design, we augment YOLOv8 through the integration of the coordinate attention module, ghost convolution module, transfer learning, and merge-nonmaximum suppression. The proposed model surpasses the original YOLOv8 and state-of-the-art models, showcasing improved accuracy and reduced computational cost. Deployed on the edge device Jetson Xavier NX, the model achieves precise PPE detection (: 92.52%) in real-time, operating at 9.11 frames per second. These findings establish a robust foundation for the efficient and real-time automated safety monitoring of construction sites, promising substantial enhancements to worker safety and data privacy within the construction industry.
Enhancing Worker Safety: Real-Time Automated Detection of Personal Protective Equipment to Prevent Falls from Heights at Construction Sites Using Improved YOLOv8 and Edge Devices
J. Constr. Eng. Manage.
Kim, Doil (author) / Xiong, Shuping (author)
2025-01-01
Article (Journal)
Electronic Resource
English
Use of Building Information Modeling in Design to Prevent Construction Worker Falls
| British Library Conference Proceedings | 2014
Use of Building Information Modeling in Design to Prevent Construction Worker Falls
| British Library Online Contents | 2014