Automatic number plate recognition (ANPR) with Yolov9 and EasyOCR

Summary

In this tutorial, Ari demonstrates how to use the YOLO v9 algorithm and Easy OCR Library for automatic number plate recognition. The video guides viewers through cloning the YOLO v9 GitHub repository, installing necessary modules, and training the model on a custom dataset. It then shows how to detect license plates using the trained YOLO v9 model and crop the detected areas. The cropped images are fed into the Easy OCR module to extract text. The process includes setting up the environment, training the model, and using Easy OCR to read license plate numbers from images and videos.

Takeaways

• 🚗 The video demonstrates how to perform automatic number plate recognition using YOLO v9 and Easy OCR.
• 📈 YOLO v9 is an advanced object detection model, while Easy OCR is a library for optical character recognition.
• 🔍 The process starts by detecting license plates using the YOLO v9 model and then cropping the detected areas.
• 📚 The cropped images are then fed into the Easy OCR module to extract the text, which represents the license plate numbers.
• 💾 To train the YOLO v9 algorithm, a dataset of images with license plates is required, which the presenter sourced from 'overflow universe'.
• 🛠️ The presenter guides through the installation of necessary modules and cloning of the official YOLO v9 GitHub repository.
• 🔧 The training process involves fine-tuning the pre-trained YOLO v9 model on a custom dataset, using a specific command in the terminal.
• 📊 The video shows how to check the model's performance through metrics like the confusion matrix and mAP (mean Average Precision).
• 📹 The detection is tested on a video, and the output is stored in a specific folder, showcasing the detected license plates.
• 🔎 After detection, a function is used to crop the license plate area from the image and pass it to Easy OCR for text extraction.
• 📝 The final step involves modifying the detect.py file to integrate the Easy OCR function for extracting text from the detected license plates.

Q & A

What is the main topic of Ari's video?

The main topic of Ari's video is demonstrating how to perform automatic number plate recognition using the YOLO v9 algorithm and Easy OCR Library.

What are the two main components used in the video to recognize number plates?

The two main components used are the YOLO v9 object detection model and the Easy OCR Library for optical character recognition.

What does YOLO v9 stand for?

YOLO v9 stands for You Only Look Once version 9, which is an object detection model.

What is the purpose of the Easy OCR Library mentioned in the video?

The Easy OCR Library is used to extract text from images, specifically to read number plates after they have been detected by the YOLO v9 model.

How does the process of number plate recognition start in the video?

The process starts by detecting license plates using the YOLO v9 model, then cropping the areas where the objects are detected, and finally using Easy OCR to extract the text.

What is required to train the YOLO v9 algorithm for number plate recognition?

To train the YOLO v9 algorithm, a dataset of images with license plates is required, which the algorithm can learn from to detect similar plates.

Where can one find the dataset used in the video for training?

The dataset used in the video can be found on a platform called 'overflow universe', and it is available for download as a zipped folder.

What is the purpose of the 'data.yml' file in the YOLO v9 project?

The 'data.yml' file is crucial as it provides the YOLO v9 algorithm with the paths to the training, validation, and test images, as well as the number and names of the classes to be detected.

How does the video demonstrate the training process of the YOLO v9 model?

The video demonstrates the training process by showing the command used to train the model, including the number of epochs, batch size, image size, and the path to the data file and pre-trained weights.

What does the 'runs' folder contain after the training of the YOLO v9 model?

The 'runs' folder contains various files and folders including the trained weights, confusion matrix, and results of the model's performance at different threshold values.

How is the detection process of license plates performed in the video?

The detection process is performed by running the 'detect.py' file with the best weight file obtained after training, and setting a confidence threshold for detection.

What modifications are made to the 'detect.py' file to integrate Easy OCR?

The modifications include creating a function to crop the detected license plate areas and use Easy OCR to extract text, and then calling this function after the detection by YOLO v9.

What is the final output of the video after integrating YOLO v9 and Easy OCR?

The final output is the successful extraction of license plate numbers from images and videos using the integrated YOLO v9 for detection and Easy OCR for text recognition.