Paper Key : IRJ************474
Author: Pragyanandan Bhagat,Mayank Satish Lakkewar,Abhineet Mishra,Tanuja Kashyap
Date Published: 11 Apr 2025
Abstract
Mining is one of the most dangerous industries, with risks including toxic gas exposure, severetemperatures, and worker accidents. To ensure worker safety, real-time environmental monitoring is required, as is stringent adherence to personal protective equipment (PPE) requirements. This study combines two safety mechanisms: a Smart Helmet equipped with IoT sensors (DHT11 for temperature and humidity, MQ-2 for gas detection, LM393 IR for object detection, a buzzer for alerts, and ESP8266 for cloud connectivity) and a PPE Detection System that uses computer vision to ensure that workers are wearing necessary protective gear such as helmets, jackets, and gloves. The Smart Helmet continuously monitors ambient conditions, identifying harmful chemicals and aberrant temperatures, and notifies users via a cloud-based system. Simultaneously, the PPE Detection System uses deep learning models like YOLOv5 and OpenCV to examine real-time video streams and determine PPE compliance. This solution improves workplace safety by integrating IoT and AI-based safety measures, lowering accident risks and providing real-time supervision. The proposed approach is tested in simulated situations to demonstrate its effectiveness in enhancing mine safety. Future improvements include increasing PPE detection to encompass more safety equipment and using sophisticated communication protocols for better real-time monitoring.
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