Object Detection using Python | OpenCV + Deep Learning | AI Computer Vision Project + Source Code
🚀 Welcome to this hands-on project on Real-Time Object Detection using Python, OpenCV, and Deep Learning!
In this video, you’ll learn how to build an AI-powered Object Detection System step by step — perfect for your final-year project or portfolio in Computer Vision and Artificial Intelligence.
👉 By the end of this tutorial, you’ll learn how to:
✅ Detect and classify multiple objects in real-time using Deep Learning
✅ Use OpenCV, TensorFlow, and YOLO/SSD models for detection
✅ Integrate a pre-trained model and visualize bounding boxes on live video
✅ Build a fully functional Object Detection Project in Python
✅ Save, analyze, and test your results with real-world data
🧩 Tech Stack Used:
🔹 Python
🔹 OpenCV
🔹 TensorFlow / Keras
🔹 Deep Learning & Computer Vision
🔹 Pre-trained COCO Dataset
💡 Why Watch This?
If you’re learning AI, ML, or Computer Vision, this project is a great way to master real-time processing, model deployment, and Python automation. Perfect for students, developers, and AI enthusiasts!
🕒 Object Counting with YOLOv7 Project Timeline
00:00 - 00:45 → Introduction and Project Outcome
00:46 - 01:45 → Overview of Object Detection and YOLOv7 Algorithm
01:46 - 02:50 → Required Libraries and Tools (time, os, cv2, torch, numpy, etc.)
02:51 - 03:45 → Understanding Model Weights and Input Sources (Image/Video)
03:46 - 05:00 → Preparing and Loading YOLOv7 Model for Inference
05:01 - 06:30 → Processing Input (Image/Video/Camera) and Preprocessing Steps
06:31 - 07:45 → Inference Pipeline: Bounding Boxes, Class Names, and Confidence Scores
07:46 - 08:50 → Applying Non-Maximum Suppression and Visualizing Results
08:51 - 09:45 → Implementing Real-Time Detection with Video and Camera Feeds
09:46 - 10:20 → Building a GUI with Tkinter for User-Friendly Interaction
10:21 - 11:11 → Running the Application, Demo, and Conclusion
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