Face Mask Detection using Python | OpenCV + Deep Learning | AI Project + Source Code

😷 Welcome to this practical AI & Computer Vision project — Face Mask Detection using Python, OpenCV, and Deep Learning! In this hands-on video, you’ll learn how to build a real-time face mask detection system using state-of-the-art AI, Computer Vision, and Deep Learning techniques. Perfect for students, developers, and researchers who want to create impactful AI projects with real-world use cases. 🧠 What You’ll Learn: ✅ Detect faces and identify if a person is wearing a mask or not ✅ Train a deep learning model for mask classification ✅ Integrate face detection and recognition using OpenCV ✅ Implement real-time detection using webcam or CCTV feed ✅ Build a complete AI-powered safety system using Python ⚙️ Technologies & Tools Used: 🔹 Python 🐍 🔹 OpenCV (Computer Vision) 🔹 TensorFlow / Keras (Deep Learning) 🔹 CNN Model Architecture 🔹 Real-Time Video Processing 📅 Project Workflow: 1️⃣ Dataset Collection & Preprocessing 2️⃣ Model Training using Deep Learning 3️⃣ Face Detection using Haar Cascade / SSD 4️⃣ Mask Classification (Mask / No Mask) 5️⃣ Real-Time Monitoring & Alerts 🕒 Face Mask Detection Project Timeline 00:00 - 01:15 → Introduction and Project Overview (Yolo V11 Model, Importance During Pandemic) 01:16 - 03:00 → Dataset Collection and Description (Roboflow Dataset, Mask vs No Mask) 03:01 - 05:00 → Preparing Google Colab Environment & Mounting Google Drive 05:01 - 07:00 → Installing Required Libraries (Roboflow, Ultralytics) 07:01 - 09:00 → Downloading and Configuring Dataset for Yolo V11 Format 09:01 - 11:00 → Visualizing Dataset Images and Data Distribution 11:01 - 13:00 → Understanding Yolo V11 Architecture and Model Training Setup 13:01 - 16:00 → Training the Model on Google Colab (Epochs, Metrics, Progress) 16:01 - 18:00 → Training Results Analysis (mAP, Precision, Recall, Loss) 18:01 - 20:00 → Downloading Trained Model Weights for Inference 20:01 - 22:00 → VS Code Setup for Inference and Project Folder Structure 22:01 - 24:00 → Required Libraries for Inference (OpenCV, Sort, Tkinter, Ultralytics) 24:01 - 26:00 → Explaining the Inference Code (Bounding Boxes, ID Assignment) 26:01 - 29:00 → Implementing Image Enhancement Techniques (Brightness, Contrast) 29:01 - 31:00 → Running Inference with Videos (Mask Detection in Real-Time) 31:01 - 33:00 → Saving Mask Detection Data as CSV and GUI Implementation 33:01 - 35:00 → Starting, Stopping the Application, and Thread Management 35:01 - 36:44 → Conclusion, Summary, and Final Remarks 💡 Why This Project Matters: Face Mask Detection is an essential AI application for public safety — widely used in hospitals, schools, airports, and workplaces. This project combines AI, Machine Learning, and Computer Vision to demonstrate the power of real-time surveillance automation. 🎓 Want this full project (source code + dataset + documentation) AND 21 more Computer Vision projects with certificate? 👉 Visit here → [ https://www.udemy.com/course/computer-vision-mastery-real-time-projects-opencv-python-ai-yolo/?referralCode=C219F7481A635FD9196A ] #FaceMaskDetection #aiprojects #pythonprojects #deeplearning #opencv #computervision #machinelearning #aidetection #facerecognition #sourcecode #aiinpython #covidsafety #scratchlearn