Published Paper Details:

The impact of a dangerous workplace on employee health and productivity has caused many firms to place a high priority on workplace safety. Employees are constantly exposed to a variety of risks at all times and places when working in today's vast construction/manufacturing complexes and other dangerous industrial locations. The incidence of accidents is therefore higher than in other industries due to the greater number of risk variables, and it is mandatory that workers wear personal protective equipment (PPE) to shield their bodies from hazardous causes. Accidents resulting from failure to wear personal protection equipment, such as safety helmets, are among the most frequent safety incidents at industrial sites. In actuality, the majority of current safety inspection procedures depend on the manual monitoring and reporting of inspectors. Observing construction sites by hand can be labour-intensive, prone to mistakes, expensive, and unsuitable for larger projects with multiple ongoing operations. Many studies on automatic helmet wearing detection and human identity identification have been published, with the goal of assisting safety inspectors on construction sites in their duties of monitoring worker safety. Another study claims that helmet wear can be integrated with person identification using computer vision. Stated differently, during helmet testing, we typically lack the ability to identify specific individuals, and vice versa. We suggest a computer vision-based approach to automatically detect workers' identity and helmet wear in order to address the aforementioned issues. Firstly, our approach combines two uses: identification and helmet wearing detection. Second, we evaluated the algorithm's accuracy and recall rate in various visual settings to determine its applicability in the actual construction site environment. This was done in accordance with the varying visual conditions on the construction site.