Real-Time Fake Number Plate Detection And Analysis With Raspberry Pi And Deep Learning

This project presents an innovative system designed for real-time detection and analysis of counterfeit number plates, combining Raspberry Pi and advanced deep learning technology, notably the You Only Look Once (YOLO) object detection algorithm. The primary objective is to bolster security and law enforcement efforts by automating the detection of fraudulent number plates, thereby enhancing traffic monitoring and maintaining the credibility of license plate recognition systems. The project begins with a comprehensive exploration of existing literature, focusing on the state-of-the-art in deep learning algorithms, particularly YOLO, and its applications in object detection tasks. Following this, extensive experimentation and prototyping are conducted to integrate YOLO with the Raspberry Pi platform, ensuring efficient on-board processing for real-time applications. The system's implementation involves the development of a robust pipeline for capturing, preprocessing, and analysing live video streams from surveillance cameras. The YOLO algorithm is then employed to detect and classify number plates within the captured frames, enabling swift identification of counterfeit plates. Our system achieved a detection accuracy of over 90% across diverse lighting conditions and plate variations.