YOLO early fire and smoke detection using deep learning yolo

Project Code :TEMBMA3705

Objective

The main objectives of this project are to detect fire and smoke at an early stage using a YOLO-based deep learning model for real-time surveillance. It processes video streams to quickly identify visual fire and smoke cues with high precision. This system aims to enable faster emergency response, minimize damage, and improve safety in indoor and outdoor environments.

Abstract

This project presents an early fire and smoke detection system using Raspberry Pi, YOLOv8, USB web camera, LCD display, relay module, DC water pump, and buzzer. The system captures real-time video images and processes them using the YOLOv8 deep learning model to detect fire and smoke accurately. When fire or smoke is detected, the buzzer alert is activated, warning messages are displayed on the LCD screen, and the relay module automatically controls the DC water pump for initial fire suppression. The proposed system provides a fast, reliable, and low-cost solution for smart fire safety and emergency monitoring applications.

Keywords: YOLOv8, Raspberry Pi, Fire Detection, Smoke Detection, Deep Learning, USB Web Camera, LCD Display, Relay Module, DC Water Pump, Buzzer Alert, Real-Time Monitoring, Smart Safety System.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.