AI-Enhanced Smart Construction Helmet: IoT Safety System with Edge Computing

Document Type

Conference Proceeding

Source of Publication

Lecture Notes in Computer Science

Publication Date

8-2-2026

Abstract

This research details the development and validation of a Smart Construction Helmet augmented with AI, IoT sensors, and edge computing. The helmet is equipped with a multi-sensor array on a Raspberry Pi 4 platform, running a YOLOv5s model for real-time Personal Protective Equipment (PPE) detection and an optimized LSTM for predictive hazard analytics. The system was rigorously evaluated in a laboratory setting designed to mimic construction site conditions. Experimental results show the system attains 88.7% accuracy in hazard detection with a 520 ms response time and can predict anomalous temperature increases 15 min ahead with 82% accuracy. The proposed framework demonstrates a viable and cost-effective approach for proactive safety management in construction environments.

ISBN

[9783032199805]

ISSN

0302-9743

Publisher

Springer Nature Switzerland

Volume

16462 LNCS

First Page

206

Last Page

214

Disciplines

Computer Sciences

Keywords

Construction Safety, Edge AI, IoT Safety, PPE Detection, Raspberry Pi, Smart Helmet

Scopus ID

105046323058

Indexed in Scopus

yes

Open Access

no

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