Accident Detection using Python | OpenCV & Deep Learning | AI Project + Source Code

Описание: 🚀 Welcome to this AI-powered Accident Detection Project built using Python, OpenCV, and Deep Learning!

In this hands-on tutorial, we’ll create a real-time vehicle accident detection system using computer vision and AI models trained on image/video data — step by step, from dataset to deployment!

🧩 What You’ll Learn:

✅ Detect and classify vehicle accidents in real-time using Deep Learning.
✅ Use OpenCV for video stream processing and frame analysis.
✅ Train a CNN model (Convolutional Neural Network) for accident detection.
✅ Integrate AI alerts for automatic accident recognition.
✅ Implement the project fully in Python, with source code included!

🧰 Tech Stack & Tools Used:

🐍 Python

🧠 TensorFlow / Keras

👁️ OpenCV

🧾 NumPy, Pandas, Matplotlib

🎥 Accident / Vehicle Detection Dataset

🎓 Perfect For:

Students, developers, and AI enthusiasts searching for:

AI & Computer Vision Projects

Deep Learning Projects in Python

Machine Learning Final Year Projects

Accident Detection Systems for Smart Cities

Real-time Detection Projects with OpenCV

🕒 Vehicle Accident Detection Project Timeline
00:00 - 02:00 → Introduction and Project Overview (Accident Detection Importance, Use of CCTV Cameras)

02:01 - 05:00 → Dataset Introduction (Kaggle Dataset), Convolutional Neural Network (CNN) Overview

05:01 - 08:00 → Google Colab Setup: Opening Notebook, Setting Runtime to GPU, Mounting Google Drive

08:01 - 12:00 → Installing Libraries and Importing Essentials (OS, CV2, NumPy, Keras, TensorFlow)

12:01 - 15:00 → Data Preprocessing: Loading Images, Grayscale Conversion, Resizing, Label Encoding

15:01 - 20:00 → CNN Model Architecture Explanation (Conv2D Layers, Batch Normalization, Max Pooling)

20:01 - 23:00 → Model Compilation, Training Setup, and Callbacks for Best Weights Saving

23:01 - 27:00 → Model Training Process (Epochs, Batch Size, Tracking Accuracy and Loss)

27:01 - 30:00 → Model Evaluation: Accuracy, Loss, Confusion Matrix, Classification Report

30:01 - 33:00 → Preparing Trained Model for Inference: Loading Weights and Prediction Approach

33:01 - 36:00 → VS Code Setup for Inference: Project Files, Required Libraries, Configuration

36:01 - 39:00 → Real-Time Video Stream Processing Logic and CNN Inference Pipeline

39:01 - 42:00 → Notification Mechanism: MQTT Setup, Server Configuration, Topic Subscription

42:01 - 45:00 → Displaying Results: Drawing Bounding Boxes, Showing Warning Text, Live Stream Encoding

45:01 - 48:00 → Mobile App Integration: Receiving Notifications, Live Stream Viewing

48:01 - 50:00 → Summary and Conclusion: Project Use Cases, Future Improvements, Community Support

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