License Plate Dectection

The Project

Our project aims to automate the registration process by accurately detecting and capturing license plate information using computer vision techniques and deep learning algorithms. Leveraging the OpenCV and NumPy libraries, our code implements a robust license plate detection system. It captures frames from a video feed, sets frame dimensions, and employs a pre-trained cascade classifier to identify license plates in grayscale frames. Detected plates are outlined with rectangles and labeled as "Number Plate". If the plate area exceeds a specified threshold, the system saves a cropped image. Real-time feedback is provided by displaying processed frames with detected plates. Users can trigger plate saving by pressing 's', with the system confirming when a scan is saved. Pressing 'q' quits the application, offering a simple yet effective solution for real-time license plate detection.

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Team Comments

I initiated a project to automate license plate detection, aiming for quick and accurate results. Manual methods are slow and error-prone, hindering traffic management. Automation enhances efficiency and accuracy, optimizing traffic flow.

Overcoming technical hurdles in license plate detection was challenging, demanding robust algorithms adaptable to diverse conditions. Balancing real-time processing with accuracy required innovative solutions. Through dedication and innovation, goal, crafting a License Plate detection.

Implementation YOLO and SSD models to boost accuracy. Integration of multiple sensing modalities for robust detection. Designing a user-friendly interface for seamless interaction and configuration. Ensured privacy protection through advanced data anonymization techniques, safeguarding user privacy.