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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 05 | May 2025

p-ISSN: 2395-0072

www.irjet.net

AI-POWERED WORKPLACE SAFETY: HELMET AND FACE DETECTION USING YOLO FOR ACCESS CONTROL SURIYA R1, SUGANTHI K2 1M. Tech Student, Department of Computer Science and Engineering, PRIST Deemed to be University, Thanjavur,

Tamil Nadu, India.

2M.E., Assistant Professor, Department of Computer Science and Engineering, PRIST Deemed to be University,

Thanjavur, Tamil Nadu, India. ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract - The influence a hazardous workplace has on worker

courses. Safety from exposure to a range of hazards can be provided by workers through the frequent use of Personal Protective Equipment (PPE), including hard hats, safety glasses, gloves, and appropriate footwear. Fall protection devices, including guardrails and personal fall arrest equipment, demand special attention, especially when undertaking work at heights. Routine maintenance and checks are necessary to ensure the safe usage of equipment and machinery. Training sessions that educate employees on proper use are also required. Proper hazard communication assists in enhancing awareness and ensures workers know about potential hazards. It can be attained through labelling, signage, and frequent safety meetings. Additionally, having a systematic approach to recording and fixing safety problems ensures a proactive safety culture on construction sites. Construction industry stakeholders could significantly enhance workplace safety and help minimize the probability of accidents and injuries by catering to these essential factors.

well-being and efficiency has led numerous companies to put extreme focus on workplace safety. Workers are continuously subjected to numerous hazards at all times and locations when laboring in today's large construction/manufacturing plants and other hazardous industrial sites. The accident occurrence is thus higher compared to other sectors because the number of variables of risk is higher, and it is also a requirement for employees to don personal protective devices (PPE) in order to protect their bodies against unsafe causes. The accidents that have occurred because employees failed to put on personal protection equipment, such as hard hats, are the most common types of safety occurrences at the worksites. In reality, most existing safety inspection processes rely on the manual observation and reporting of inspectors. Hand observation of construction sites can be time-consuming, error-prone, costly, and inappropriate for large projects with several simultaneous operations. There have been numerous publications of studies on automatic detection of helmet wearing and human identity recognition, which have been aimed at helping safety inspectors on construction sites in monitoring workers' safety. Another study asserts that the computer vision-based person identification could be combined with helmet wear. In other words, in helmet testing, we usually do not have the capability to recognize individual people, and vice versa. We propose a computer vision approach to automatically recognize workers' identity and helmet wear to resolve the issues discussed above. First, our method involves two applications: identification and detection of helmet wear. Second, we tested the accuracy and recall rate of the algorithm under different visual environments to establish its use in the real construction site conditions. This was carried out as per the differing visual conditions at the construction site.

In most sectors, particularly construction where there is a high risk of head injuries, helmets are a critical part of employee safety. The primary function of helmets is to prevent the risk of severe head injury by serving as a protective barrier against possible threats such as falling objects, debris, or impact. Additionally, helmet usage is in line with occupational health and safety regulations in many locations, which demonstrates the importance as an obligatory protection measure. The helmets consist of a hard exterior, typically made from fiber glass or high-density polyethylene. This outer hard surface serves to spread force of impact across a bigger surface area, thereby making the helmet more effective. Suspension system, with straps and headband, is typically integrated in the interior in order to facilitate a comfortable as well as a secure fit around the head of the wearer. There are a variety of helmet types that exist and are created for specific sorts of work conditions. Industrial helmets, for instance, are manufactured to be able to withstand electricity conductivity, as well as brimmed construction helmets that help protect against sunshine and rain. Protecting workers from risks associated with heads, the combination of different helmets' features and designs together aims to provide an improved work condition. Fig 1 illustrates different colors of construction site helmets with code.

Key Words: Accident Occurrence, Computer Vision, Helmet Detection, Personal Protective Equipment, Safety Inspection, Worker Identification

1.INTRODUCTION In order to reduce the risks involved with working under such circumstances, it is important to maintain worker safety on building and construction sites. It is advisable that all employees undergo extensive training programs regarding key standards and be given frequent refresher

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