How Cal Fire uses AI fire detection technology Circuit Diagram IoT-Based Fire Detection System 🔥🚨. A smart fire detection system using ESP32, flame sensors, and a mobile app. Detects flames, monitors temperature and gas levels, and sends real-time alerts with user location to the fire department via Java Servlet and WebSocket communication. AI-powered developer platform Available add-ons
During urban fire incidents, real-time videos and images are vital for emergency responders and decision-makers, facilitating efficient decision-making and resource allocation in smart city fire monitoring systems. However, real-time videos and images require simple and embeddable models in small computer systems with highly accurate fire detection ratios. YOLOv5s has a relatively small model Traditional fire detection methods often rely on manual monitoring or conventional image analysis techniques, which can lead to delayed detection and lower accuracy. To address these challenges, this project implements an AI-powered fire detection system using the yolo8 object detection model. The model was evaluated using two augmented datasets: Dataset A and Dataset B, which consist of fire and non-fire images sourced from multiple video and image datasets. FireNet-CNN's architecture, which includes 2.75 million parameters and a compact model size of 10.58 MB, has been meticulously optimized for fire detection tasks.
Time Fire Detection System for Urban Fire ... Circuit Diagram
Fire disasters pose significant risks to human life, economic development, and social stability. The early stages of a fire, often characterized by small flames, diffuse smoke, and obstructed A real-time flame detection algorithm combining Flask, OpenCV, and YOLOv5 to offer advanced fire monitoring and alerts. Flame AI integrates live video analysis, machine learning for accurate detection, and instant notification systems, enhancing emergency response across various environments. - sswadkar/hackalytics
This project develops a Fire Control System using CNN and Arduino for real-time fire detection and suppression. A Convolutional Neural Network (CNN) processes live video feeds to identify fire, triggering the Arduino to activate extinguishing mechanisms. The system provides an efficient, automated solution for fire safety in critical environments.
bantubhaskar/AI Circuit Diagram
The proposed system uses deep learning for real-time fire detection in videos, enhancing accuracy and adaptability. OpenCV aids in crowd counting, improving situational awareness during emergencies. This innovation promises to revolutionize fire safety by offering timely and precise detection, potentially saving lives and property. viAct's AI in fire and smoke detection stands at the forefront of fire safety innovation, offering a comprehensive AI-powered solution designed for real-time detection and prevention. Equipped with the capabilities of Large Language Modelling (LLMs), Generative AI, Computer Vision for fire detection, and AI video analytics, this fire safety
