Major Project · Web Development

Fog Detection Syatem Project

The AI-Based Fog Detection and Alert System is a computer vision application that analyzes uploaded video footage and detects the presence of fog using a trained deep learning model. The system processes video frames, predicts …

Difficulty Level

Beginner

Core Modules

3 Modules

Project Overview

Fog is a major environmental factor that reduces visibility and increases the risk of road accidents. This project presents an AI-based Fog Detection and Alert System that utilizes Deep Learning and Computer Vision techniques to identify foggy conditions from video footage. Users upload a video through a web interface, and the system extracts and processes individual frames using OpenCV. A pre-trained TensorFlow/Keras model analyzes each frame and predicts the probability of fog presence.

The detected fog percentage is calculated for every frame, and an overall average fog level is generated for the entire video. If the fog density exceeds a predefined threshold, the system automatically sends an email alert to the concerned authority, enabling timely preventive measures. The project demonstrates the integration of Artificial Intelligence, Image Processing, and Web Technologies to develop an intelligent weather monitoring and safety system.

Module Breakdown 3 Modules

Video Upload Module

Allows users to upload video files through the web interface. The uploaded videos are stored securely on the server for processing.

Fog Detection Module

Determines whether fog is present based on prediction results and calculates fog percentages.

Result Analysis Module

Computes the average fog percentage across all processed frames and generates the final result.

Technology Stack

Python HTML CSS3 Java Script Django SQLite

Project Screenshots 2 Screenshots

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Project Abstract

Fog is a major environmental factor that reduces visibility and increases the risk of road accidents. This project presents an AI-based Fog Detection and Alert System that utilizes Deep Learning and Computer Vision techniques to identify foggy conditions from video footage. Users upload a video through a web interface, and the system extracts and processes individual frames using OpenCV. A pre-trained TensorFlow/Keras model analyzes each frame and predicts the probability of fog presence. The detected fog percentage is calculated for every frame, and an overall average fog level is generated for the entire video. If the fog density exceeds a predefined threshold, the system automatically sends an email alert to the concerned authority, enabling timely preventive measures. The project demonstrates the integration of Artificial Intelligence, Image Processing, and Web Technologies to develop an intelligent weather monitoring and safety system.

Project Kit Includes

Complete Source Code

Database Schema & Setup Guide

Project Report Template

Completion Certificate

Installation & Deployment Guide

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Fog is a major environmental factor that reduces visibility and increases the risk of road accidents. This project presents an AI-based Fog Detection and Alert System that utilizes Deep Learning and Computer Vision techniques to identify foggy conditions from video footage. Users upload a video through a web interface, and the system extracts and processes individual frames using OpenCV. A pre-trained TensorFlow/Keras model analyzes each frame and predicts the probability of fog presence. The detected fog percentage is calculated for every frame, and an overall average fog level is generated for the entire video. If the fog density exceeds a predefined threshold, the system automatically sends an email alert to the concerned authority, enabling timely preventive measures. The project demonstrates the integration of Artificial Intelligence, Image Processing, and Web Technologies to develop an intelligent weather monitoring and safety system.