Our Instructors Collaborate With Top Tech Leaders
Startups
Microsoft
NVIDIA
Develop an AI-powered traffic sign detection system using YOLOv7
that identifies and classifies various traffic signs in
real-time from live video feeds or recorded footage.
Intermediate-level. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to build AI-powered accident detection systems and secure computer vision jobs or freelance projects.
Understand how AI recognizes and classifies traffic signs for intelligent transport systems.
Use Python with OpenCV and Convolutional Neural Networks (CNNs) to train and detect traffic signs.
Build a real-time system that detects and identifies traffic signs from live video input.
Save recognized traffic sign data with timestamps into a database for analysis or reporting.
Create a user interface to show live detection results and traffic sign info clearly.
Finish the course and receive a verified certificate of success.
Muhammad Yaqoob is the founder of Tentosoft Pvt Ltd and a seasoned Computer Vision expert. With 10+ years of experience and over 5,000+ students taught globally, he brings deep industry knowledge and a passion for practical, hands-on learning.
Gain practical experience by building industry-relevant computer vision applications from scratch.
Discover the fundamentals of building an AI-powered traffic sign detection and recognition system using computer vision to identify and classify traffic signs in real-time.
Course Introduction and Features
Detect traffic signs
Useful in smart vehicles
Set up your Python development environment with essential tools and libraries like OpenCV and TensorFlow to enable real-time traffic sign detection.
Installing Python
VS Code Setup for Python Development
Learn to configure Google Colab for cloud-based development, providing a hassle-free environment for training and testing your traffic sign detection model.
Google Colab Setup
No install needed.
Ideal for AI projects
Install critical Python packages such as OpenCV, TensorFlow, and NumPy to support the development of a robust traffic sign detection and recognition system.
Packages Installation
Prepare coding environment
Ready system for AI tasks
Quick setup process
Prepare and preprocess datasets of traffic sign images to ensure accurate training and validation of your AI model for real-world detection scenarios.
Dataset Preparation
Label objects clearly
Ready data for training
Develop utility functions to streamline the traffic sign detection process, including drawing bounding boxes and preprocessing images for model input.
Simplify main code
Draw boxes on signs
Support detection logic
Implement a custom loss function to optimize the performance of your traffic sign detection model, ensuring accurate classification and localization.
Loss function
Tune model performance
Track learning progress
Explore the EfficientNet-B0 model, a lightweight and efficient architecture, and learn how to implement it for real-time traffic sign recognition.
EfficientNet-B0 Model Implementation Info
Lightweight and fast.
Ideal for mobile apps
Is This Course Right for You?
Kickstart your AI journey with structured, hands-on learning.
Build a portfolio that recruiters can't ignore.
Add powerful AI/CV features to your apps and software.
Upskill for higher-paying, future-ready tech roles.
Build Smarter, more intelligent applications.
Transition into AI even with zero background.
One-time payment for lifetime access to all course materials and updates
Get hands-on experience with real-world projects designed to sharpen your technical skills and build your confidence. Each project is crafted to help you apply concepts practically, write cleaner code, and prepare for real developer challenges.
Highly recommended for small teams who seek to upgrade their time & perform.
₹ 6720 inclusive of GST ₹ 13999
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