What My Project Does:

I’ve developed an advanced image detection system designed to recognize and process license plates from live video feeds and images. The system leverages YOLOv11 for object detection, PaddleOCR for text recognition, and SQLite for storing detected license plates.

The model has been custom-trained using a Roboflow dataset of 400 images, yielding highly accurate results. It also incorporates various techniques to handle challenges like low image resolution, motion blur, and varied lighting conditions.

Target Audience:

This project is ideal for anyone working with image detection, especially those who want to enhance or build upon their existing license plate recognition systems. It is well-suited for developers, hobbyists, or students learning about computer vision and OCR. Additionally, it can be useful for industries requiring automated license plate recognition, such as security, parking management, or toll systems.

Comparison:

What sets DeepPlate apart from existing alternatives is its combination of advanced techniques and robust tools to improve both detection and text recognition:

Preprocessing Techniques: The system uses contrast enhancement, Gaussian blur, noise reduction, and other techniques to significantly improve image quality before OCR is applied. Advanced OCR Capabilities: By using PaddleOCR, it can accurately detect license plates even at different angles, overcoming one of the most common challenges in license plate recognition. Dynamic Image Upscaling: The system includes a method for scaling up small images, ensuring that license plates are enlarged to a target size, improving OCR accuracy without being constrained by low resolution. Efficient Detection: YOLOv11 optimizes the detection process, focusing on license plates with minimal false positives and allowing for faster processing times.

While other systems might struggle with plate orientation, image quality, or motion blur, DeepPlate addresses these issues with its combination of advanced tools and techniques, making it possibly a more reliable solution in real-world applications.

https://github.com/Tristan296/DeepPlate/tree/main