Construction Site Worker’s Safety Detection System

The construction sites are one of the most dangerous workplaces, due to the presence of machinery, tall buildings, and unsafe practices at work. One of the main factors leading to accidents is the incorrect use of personal protective equipment (PPE) like safety helmets, safety glasses, safety footwear, and reflective jackets by employees. Besides, there are cases of undetected health problems that lead to injuries. Traditional approaches to safety monitoring involve human inspection and periodical examination of workers, which are time-consuming, prone to errors, and do not work effectively on large-scale construction sites. The goal of this research is the development of an intelligent worker safety detection algorithm for the smart construction site that would incorporate machine vision–based detection of PPEs and Internet of Things (IoT) technologies for health monitoring. The algorithm detects safety helmet and reflective jacket using machine vision based on deep learning algorithms, while body temperature is detected using a DS18B20 sensor interfaced with an ESP32 microcontroller. A decision-making mechanism confirms safety of the conditions and sends a warning message in case any violation or abnormal situation is observed.