Yolov8 Object Detection For Number Plate Recognition

Collect and Label Data, Train YOLOv8 Model, Implement OCR to Recognize Text, Integrate with a Streamlit Web App

What you'll learn

Set up your environment for object detection

Learn how to recognize number plates in images and videos using OCR

Collect and label a custom dataset for training the YOLOv8 model

Integrating the number plate recognition system with a Streamlit web application

Train the YOLOv8 model and learn how to use it to detect number plates in images and videos

Requirements

Basic knowledge of Python programming, OpenCV, and computer vision.

Description

In this comprehensive course, you'll learn everything you need to know to master YOLOv8. With detailed explanations, practical examples, and step-by-step tutorials, this course will help you build your understanding of YOLOv8 from the ground up.Discover how to train the YOLOv8 model to accurately detect and recognize license plates in images and real-time videos.From data collection to deployment, master every step of building an end-to-end ANPR system with YOLOv8.What you'll get:Here's what you'll get with this course:3 hour of HD video tutorialsSource code used in the courseHands-on coding experience and real-world implementation.Step-by-step guide with clear explanations and code examples.Gain practical skills that can be applied to real-world projects.Lifetime access to the coursePriority supportWhat is covered in this course:Just so that you have some idea of what you will learn in this course, these are the topics that we will cover:Set Up Your Environment for Object DetectionCollect the Data for Training the ModelTrain the YOLO Model and Learn How to Use it to Detect Number Plates in Images and Video StreamsLearn How to Recognize Number Plates in Images and Videos Using OCRIntegrating the Number Plate Recognition System with a Streamlit Web Application

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: What is Object Detection

Lecture 2 What is Object Detection

Section 3: Advancements in Object Detection

Lecture 3 Advancements in Object Detection

Section 4: YOLO: The Object Detection Framework

Lecture 4 What is YOLO

Lecture 5 How YOLO works

Lecture 6 YOLO Architecture

Lecture 7 YOLO Versions

Section 5: Environment Setup

Lecture 8 Install Miniconda

Lecture 9 Install the Required Packages

Lecture 10 Install CUDA and cuDNN for GPU support

Lecture 11 Project Structure

Section 6: Data Preparation

Lecture 12 Introduction

Lecture 13 Gathering the Data

Lecture 14 Labeling the Data

Lecture 15 Splitting the Data

Lecture 16 Creating the YAML File

Section 7: Training the YOLOv8 Model

Lecture 17 Choose a Model

Lecture 18 Start Training

Section 8: Detecting Number Plates with the Trained Model

Lecture 19 Number Plate Detection in Images

Lecture 20 Number Plate Detection in Videos

Section 9: Recognizing Number Plates Using OCR

Lecture 21 Number Plate Recognition in Images

Lecture 22 Number Plate Recognition in Videos

Section 10: Create a Web Application with Streamlit

Lecture 23 Creating a New Streamlit App

Lecture 24 Adding Upload Feature

Lecture 25 Integrating our Number Plate Recognition System with Streamlit

Section 11: Conclusion

Lecture 26 Conclusion

Python programmers who are looking for a practical, hands-on guide to building more advanced object detection and recognition projects.,Anyone familiar with OpenCV and computer vision who wants to take their skills to the next level and learn how to apply object detection to solve real-world problems.