Safety Gear Compliance Detection Using Data Augmentation-Assisted Transfer Learning in Construction Work Environment

Abstract

The study provides a practical solution to the concern of detecting safety gear compliance in construction. This is imperative given that safety in the construction work environment is one of the greatest global concerns, and advancements in deep learning algorithms, especially in the area of machine learning and database management, enable the possibility to address this challenge in construction. This study developed a framework to recognize construction personnel's safety compliance with PPE, which is designed to be implemented into an organization's operational procedure. The Convolutional Neural Network model was constructed by employing machine learning to a basic version of the YOLOv3 deep learning model for the study. On the testing data, the detection method generated an F1 score of 0.9299, with a mean precision-recall rate of 92.99 %. The purpose of this study is to testify to the viability and applicability of machine vision-based methodologies for automated safety-related compliance processes on construction sites.