Smart Communication System for Human Life Safety

Abstract

Safety helmets may provide sufficient protection for construction workers. But employees frequently take off their helmets due to discomfort and a lack of security knowledge, leaving them vulnerable. Workers who don't wear protective headgear are more likely to be hurt in accidents involving falling items (including humans) and vertically falling things. A fast and reliable safety helmet detector is, however, desperately needed. Observing workers to ensure they are wearing protective headgear is a crucial aspect of site management. However, the commonplace manual monitor calls for a much work, and it's tough to get people to adopt new methods for installing sensors in safety helmets. This study provides a Deep Learning (DL) based safety helmet recognition method that is both fast and accurate. Using deep learning, a system has been developed to detect hard hats in construction zones. To solve this problem, the SSD-Mobile Net technique uses convolutional neural networks. Photographs of safety helmets taken either manually from a company's video surveillance system or automatically through a web crawler may be made available to the general public. The picture set includes a training, a validation, and a test set. Our findings show that a deep learning model built using the SSD-Mobile Net method can reliably identify potentially hazardous behaviours on a construction site, such as the removal of a hard cap.