Intelligent Human Intrusion & Object Detection System using YOLOv7, Python & Computer Vision
Welcome to the Real-Time Intrusion & Object Detection with YOLOv8, YOLOv7-Tiny, and Python Course!
What you’ll learn
- Learn object detection fundamentals and its applications in intrusion detection, surveillance, and real-world domains using AI and computer vision..
- Set up a Python environment with essential libraries like Tkinter, OpenCV, and PyTorch for computer vision and object detection tasks..
- Understand object detection concepts and how they’re used in monitoring unauthorized intrusions via video streams in real-time scenarios..
- Use YOLOv8 and YOLOv7-Tiny models for accurate, real-time object and human intrusion detection using lightweight and efficient algorithms..
- Load and configure YOLOv8 and YOLOv7-Tiny pre-trained weights to enable real-time, high-accuracy detection of objects and intruders..
- Preprocess video streams and images to integrate smoothly with YOLO models for real-time monitoring and effective object detection..
- Write Python scripts to detect objects and intruders, extracting bounding boxes, class labels, and confidence scores for interpretation..
- Visualize detection results by drawing bounding boxes, adding labels, and showing confidence scores on video frames for better insight..
- Optimize YOLOv7-Tiny for real-time performance on devices with limited resources without compromising detection speed or accuracy..
- Tackle challenges like low-light detection, occlusions, motion blur, small or overlapping objects in object and intrusion detection..
- Apply AI-based intrusion detection in restricted zones, industries, homes, offices, and public places to improve safety and surveillance..
Course Content
- Introduction of the Object Detection using yolov7 –> 1 lecture • 1min.
- Environment Setup for Python Development –> 2 lectures • 3min.
- Object Detection Project Overview –> 1 lecture • 1min.
- Understanding Key Packages for Object Detection –> 1 lecture • 1min.
- Understanding the YOLOv7-tiny Model Weights –> 1 lecture • 1min.
- Real-Time Object Detection with YOLOv7-tiny –> 1 lecture • 4min.
- Building a Tkinter GUI for Real-Time Object Detection –> 1 lecture • 2min.
- Executing Real-Time Model Inference for Object Detection –> 1 lecture • 3min.
- Environment Setup for Python Development –> 2 lectures • 3min.
- Launching VS Code from the Command Line –> 1 lecture • 1min.
- Managing Folders and Files of the Project –> 1 lecture • 1min.
- Understanding and Setting Up Required Packages –> 1 lecture • 2min.
- Accessing and Using Polygon Coordinates for Tracking –> 1 lecture • 1min.
- Key Variables and Their Role in YOLOv8 –> 1 lecture • 2min.
- Model Inference for Intrusion Detection –> 1 lecture • 6min.
- Tkinter Implementation for Real-Time Intrusion Detection –> 1 lecture • 3min.
- Getting Polygon Coordinates Using Roboflow for Intrusion Detection –> 1 lecture • 2min.
- Intrusion Detection Code Execution –> 1 lecture • 6min.
- Wrapping Up –> 1 lecture • 1min.
Requirements
Welcome to the Real-Time Intrusion & Object Detection with YOLOv8, YOLOv7-Tiny, and Python Course!
In this comprehensive hands-on course, you’ll learn how to build real-time human intrusion detection and object detection systems using the powerful YOLOv8 and YOLOv7-Tiny algorithms, Python, and Tkinter. This course combines the strengths of AI and computer vision to help you design efficient and interactive detection systems for various real-world applications.
What You’ll Learn:
- Set Up Your Python Development Environment: Learn to set up your development environment and install essential libraries like OpenCV, Tkinter, and PyTorch for building both intrusion and object detection systems.
- Utilize Pre-trained YOLOv8 & YOLOv7-Tiny Models: Master using pre-trained YOLOv8 for intrusion detection and YOLOv7-Tiny for object detection, both of which provide high accuracy even in complex environments.
- Preprocess Video Streams for Optimal Performance: Learn how to preprocess live video feeds and images for optimal performance with both models to ensure seamless and accurate detection.
- Build Interactive Tkinter GUIs: Create and implement Tkinter-based GUIs to visualize real-time detection results, displaying alerts and identified intruders or detected objects.
- Address Real-World Challenges: Tackle common challenges in both object and intrusion detection, such as low-light conditions, occlusions, and high-traffic environments, ensuring the accuracy and reliability of your system.
- Optimize for Real-Time Performance: Master techniques to ensure fast and efficient processing of live video streams for real-time monitoring in both intrusion detection and object tracking scenarios.
- Handle Complex Surveillance Environments: Learn how to manage detection in diverse environments, including varying lighting, camera angles, and crowded areas to ensure robust and accurate tracking results.
By the End of This Course:
You’ll have developed a fully functional AI-powered system that detects unauthorized human activity and objects in real time, using interactive visualization through Tkinter-based GUIs. Whether you’re working on security solutions for restricted areas, industrial sites, or public spaces, you will have a comprehensive understanding of deploying advanced AI models in real-world applications.
This course is perfect for beginners or those with experience in computer vision and AI, and it will equip you with practical knowledge to build cutting-edge surveillance and detection systems.
Enroll now and unlock the potential of YOLOv8 & YOLOv7-Tiny for impactful detection solutions!