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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 (mAP50: 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.
Kim et al. (Tue,) studied this question.