Automated Hardhat Detection for Construction Safety Applications

Abstract

Despite various safety inspections carried out over the years to ensure compliance with regulations and maintain acceptable and safe working conditions, construction is still among the most dangerous industries responsible for a large portion of the total worker fatalities. A construction worker has a chance of 1-in-200 of dying on the job during a 45-year career, mainly due to fires, falls, and being struck or caught-in/between objects. This in part can be attributed to how monitoring the presence and proper use of personal protective equipment (PPE) by safety officers becomes inefficient when surveying large areas and a considerable number of workers. Therefore, this paper takes the initial steps and aims at evaluating existing computer vision techniques, namely object detection methods, in rapidly detecting whether workers are wearing hardhats from images captured on many indoor jobsites. Experiments have been conducted and results highlighted the potential of cascade classifiers, in particular, in accurately and precisely detecting hardhats under various scenarios and for repetitive runs.