A Lightweight License Plate Recognition Method Based on YOLOv8

To address the challenges faced by license plate recognition systems in certain scenarios—such as complex plate backgrounds, plate tilt, and the large model size that hinders deployment on resource-constrained devices—this paper proposes a compact and computation-efficient license plate detection algorithm that maintains the required recognition accuracy while being easy to deploy on edge computing devices. Experimental results show that the optimized detection model reduces network parameters by approximately 32% without compromising accuracy, while the model file size is similarly reduced by about 32%, significantly conserving device resources. For the recognition stage, LPRNet is further optimized. Experiments demonstrate that the improved recognition network achieves a 1.2% higher accuracy compared to the baseline, with almost no increase in model size, thereby delivering better license plate recognition performance. The combined detection and recognition models occupy less than 6 MB of storage, offering clear advantages in recognition rate, robustness, and resource-efficient design, making them well-suited for deployment on edge devices.