Forest fire detection using Convolutional Neural Networks

his project implements a Forest Fire Detection and Alert System using sensor data (temperature, humidity, and smoke levels) to monitor forest fire risks.

Wildfire is a natural disaster, causing irreparable damage to local ecosystem. Sudden and uncontrollable wildfires can be a real threat to residents’ lives. Statistics from National Interagency Fire Center (NIFC) in the USA show that the burned area doubled from 1990 to 2015 in the USA. Recent wildfires in northern California (reported by CNN) have already resulted in more than 40 deaths and 50 missing. More than 200,000 local residents have been evacuated under emergency. The wildfires occur 220,000 times per year globally, the annual burned area is over 6 million hectares. Accurate and early detection of wildfire is therefore of great importance. Fire detection task is crucial for people safety. Several fire detection systems were developed to prevent damages caused by fire. One can find different technical solutions. Most of them are sensors based and are also generally limited to indoors. They detect the presence of particles generated by smoke and fire by ionization, which requires a close proximity to the fire. Consequently, they cannot be used in large covered area. Moreover, they cannot provide information about initial fire location, direction of smoke propagation, size of the fire, growth rate of the fire, etc. To get over such limitations video fire detection systems are used

A Model for the Early-Detection of Forest-Fires

This is a GUI interface which can process forest fire detection, smoke detection and fire segmentation. Yolov5 is used to detect fire and smoke and unet is used to segment fire.

Real-time forest fire detection using satellite data (MODIS, VIIRS) and CNN-based classification.

online early forest fire detection system based on drone platform.

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Forest Fire Detection By Convolutional Neural Network

The current forest surveillance systems methods consume a lot of resources and are less efficient, not reliable and require a constant human presence whose tasks can be easily automated using new technology. To solve these problems we propose an autonomous surveillance system which uses object detection to identify specified animals. It is capable of monitoring forest fires, intruders, wildlife etc, all at once and alerts the concerned officials immediately and precisely. It has a hybrid object detection system using HAAR and Backpropagation neural network algorithms which can be used to train and detect animals and predict from the data obtained respectively. This helps in detecting various unwanted visitors, dangerous animals, or restricted tools into the forest. The system can not only store the video feed but can also determine population , track a specific animal or human and sends the pictures to your email directly along with real-time video monitoring via the internet which allows the users to monitor from anywhere in the world and sends instant alerts to your phone via an SMS even in remote areas in case of emergencies, and it stores all the data in a repository. We can control the system using a windows app which allows us to select which animals to be detected by the camera modules and their alert levels along with other settings and also provides a detailed analysis on various things like forest fires, animal population, trespassed areas etc, to users in simple charts. It is a smart, automatic, modular system which is cheap and easily expandable.

Forest Fire Detection using Mask RCNN Model

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Computer Vision project focused on detecting smoke and fire in wild environments. The Google Vision Transformer was fine-tuned on a custom dataset.

Fire Eye is a robust and fixed lookout system capable of real-time forest fire and smoke detection. We have use Flask Python web framework to develop the user interface of our application.

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A real-time deep learning system powered by YOLOv8 for accurate fire and smoke detection across images, videos, and live webcam feeds. The system issues instant Telegram alerts when fire confidence surpasses a safe threshold. With a Flask-based interface and detailed model evaluation metrics, this solution enhances wildfire monitoring, early detect

cupcarbon project

Forest-Fire detection model using Tensorflow and keras.

Detection of Forest Fires using Satellite Imagery and Machine Learning.

PyroScan AI is a GenAI-powered multi-agent system for real-time forest fire prediction across 10 global zones. Designed for researchers and disaster teams, it includes agents for fire detection, weather analysis, and historical data mining. Deployable via CLI or Docker.

A convolutional neural network(CNN) that detects forest fires.

Comprehensive Analysis of Forest Fire Detection using Deep Learning Models and Conventional Machine Learning Algorithms

CNN based Forest Fire Detection for camera equiped edge devices

This project utilizes 🔬 machine learning algorithms to predict 🔥 forest fires using inputs such as 🌡️ temperature, 🧪 oxygen level, and 💧 humidity. The trained model can analyze the inputs and provide a prediction of the likelihood of a forest fire occurring. The prediction results are displayed through a 🌐 Flask web application.

Forest Fire Detection Research involves the development of a computer vision-based system designed to detect forest fires. It leverages object detection techniques to identify visual signs of fire and smoke in images, aiming to support early warning systems and wildfire prevention efforts.

Forest fire detection from satellite imagery using deep learning

Forest fire detection by fine tuning MobileNetV2

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Model created using InceptionV3 to recognize the pattern in images for the characteristics of forested areas with and without fire hazard occurrence. The model is tested on a real time video feed ; a video input is segmented into images and fed into the model to make final evaluations.

This project is an attempt to use CNN to detect the presence or the start of a forest fire in an image. The idea is that this model could be applied to detect a fire or a start of a fire from (aerial) surveillance footage of a forest. The model could be applied in real-time to low-framerate surveillance video and give alert in case of fire.