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Enhancing Construction Worker Safety through YOLOv8-Based PPE Detection and Sensor-Driven Smart Helmets

Year: 2025

Type: article

Abstract: In order to improve worker safety in construction settings, this study proposes an integrated safety system that combines real-time sensor monitoring with deep learning. To ensure adherence to PPE regulations, the first component uses the YOLOv8 algorithm for precise and effective helmet detection. In order to monitor motion and environmental threats and send real-time alerts, the second component includes a Smart Helmet that is outfitted with a MEMS sensor, gas sensor, DHT11, and GSM module. When combined, these modules provide enhanced on-site safety and a proactive approach to accident avoidance.

Source: International Journal for Research in Applied Science and Engineering Technology

DOI: 10.22214/ijraset.2025.69435

Author: Mr. B. Dillibabu

Language: English

Topic: Occupational Health and Safety Research

Subfield: Radiological and Ultrasound Technology

Field: Health Professions

Domain: Health Sciences

SDGs: Industry, innovation and infrastructure, Decent work and economic growth, Sustainable cities and communities

Open Access status: diamond

OpenAlex ID: W4409838222

FWCI: 0.882

Cites:

Cited by: 1

Related to: 10