AI-Powered Driver Distraction Detection System Python |Deep Learning Project + Source Code | Tamil

🚗 Welcome to this hands-on AI-Powered Driver Distraction Detection System using Python, OpenCV, and Deep Learning!

In this Tamil explained (தமிழில் விளக்கப்படும்) tutorial, you’ll learn how to build a real-time computer vision system that detects driver distractions such as texting, yawning, drowsiness, and looking away using AI.
This project is highly useful for Tamil engineering students, final-year projects, AI safety research, and smart vehicle systems.

🔍 What You’ll Learn

✅ Real-time driver monitoring using Computer Vision
✅ Deep Learning techniques for face & eye detection
✅ Training & testing your own distraction detection model
✅ Integration of OpenCV & TensorFlow for AI automation
✅ Step-by-step explanation of code, logic & dataset handling

🧰 Tech Stack Used

💻 Languages & Tools:
Python, OpenCV, TensorFlow / Keras, NumPy, Deep Learning, Computer Vision

🧠 Core Concepts:
CNN (Convolutional Neural Networks), Object Detection, Image Classification, Real-time Monitoring

🕒 Driver Distraction Detection – Full Project Timeline

00:00–01:20 → Project Outcome
Overview of what the system detects (phone usage, drowsiness, no-attention) and final results.

01:20–03:50 → Introduction
Why driver distraction detection is important, real-world applications.

03:50–07:00 → System Requirements
Python version, required libraries, camera setup, hardware basics.

07:00–11:30 → Environment Setup
Installing Python, dependencies (OpenCV, Dlib/MediaPipe/YOLO), and project folder structure.

11:30–15:00 → Dataset Overview
Driver images/videos, distracted vs non-distracted classes, annotation format.

15:00–19:00 → Model Setup (YOLO / CNN / MediaPipe)
Cloning repo, loading model weights, configuring parameters.

19:00–24:00 → Facial & Gesture Landmark Detection
Eyes, head pose, mouth, hand-to-face detection, attention points.

24:00–32:00 → Distraction Logic Implementation
Phone usage detection, yawning, drowsiness, looking away, unsafe actions.

32:00–40:00 → Real-time Driver Monitoring System
Webcam feed analysis, bounding boxes, alerts on screen, FPS optimization.

40:00–47:30 → Alert & Warning System
Beep alerts, on-screen notifications, event logging, threshold tuning.

47:30–54:00 → Testing & Evaluation
Testing different scenarios, accuracy metrics, false positive control.

54:00–56:44 → Conclusion
Final results, improvements, next steps.
🧩 Why Watch This Project?

Learn how AI & Deep Learning improve road safety by detecting distracted drivers — a must-have skill for:

AI & Computer Vision learners

Automotive AI developers

Final-year students

Real-time detection system builders

⭐ Get Full Source Code + 21 Computer Vision Projects (For Tamil Students)

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