Automatic Number Plate Recognition: A Deep Dive into YOLOv8 and ResNet-50 Integration

The recognition of cars is vital for the control and surveillance systems. Manually recognizing all the car number plates that are passing or parked is a laborious and complex task for humans. The task of identifying number plates under Indian conditions where number plate standards are rarely followed is examined in this research. Unlike conventional approaches limited to specific conditions, the Proposed model offers adaptability for diverse scenarios, including different illuminations and angles, ensuring robust performance in varied environments. The proposed model, trained on the Indian car number plate dataset, utilizes YOLOv8 for efficient object detection and RestNet-50 for powerful feature extraction, enhancing ANPR accuracy and robustness. The system can localize single-line number plates with a success rate of about 98.6% and recognize characters with a success rate of about 97.81% under widely varying illumination conditions.