YOLOv8 Object Detection for Number Plate Recognition

Explore what object detection is, how it locates objects with bounding boxes using CNNs and deep learning, and its real world uses in surveillance, security, and self-driving cars.

Explain how YOLO works by dividing an image into an s by s grid, predicting multiple bounding boxes, confidence scores, and class probabilities per cell.

Install required packages for the yolov8 number plate project, including label studio, Ultralytics, easy OCR, streamlit, OpenCV, scikit-learn, and Yaml, after activating the environment and preparing for Cuda and cuDNN.

Enable gpu-accelerated training for YOLOv8 by installing cuda and cudnn via Miniconda, configuring drivers, and setting environment variables.

Explore the project structure for a custom yolov8 license plate workflow, including datasets and car number plate folders, preprocessing.py, train/val/test split, and the web app with App.py.

Gather data to train the YOLOv8 model for number plate recognition by illustrating three options—web scraping, public datasets, and manual Google image searches—and annotate bounding boxes for training.

Gather a diverse dataset of car number plates from Flickr by downloading about 200 images with an image downloader, then manually remove non-plate images to prepare for labeling and OCR.

Draw boxes around number plates in images with Label Studio and export annotations in yolo format, detailing normalized center x, center y, width, and height for single class number plate.

Split the data into training, validation, and testing sets using a 70/10/20 ratio with a Python script and train test split.

Create a yaml configuration with train, valid, and test paths and the number plate class, then write the dictionary to number plate.yaml using Python and yaml.