License Plate Detection Based on Improved YOLOv8n Network

To address the challenges of complex backgrounds, varying target scales, and dense targets in license plate detection within surveillance scenarios, we propose an enhanced method based on an improved YOLOv8n network. This approach involves redesigning key components of the YOLOv8n architecture, including the C2f module, the SPPF module, and the detection head. Additionally, we optimize the WIoU loss function, replacing the original CIoU loss function, which leads to improved bounding box feature extraction and enhanced regression accuracy. To evaluate the model’s robustness in complex environments with varying lighting, backgrounds, angles, and vehicle types, we created a custom surveillance license plate dataset. Experimental results show that the improved model achieves a notable increase in detection accuracy, with [email protected] rising from 90.9% in the baseline model to 94.4%, precision improving from 90.2% to 92.8%, and recall increasing from 82.9% to 87.9%. Additionally, the model’s parameters are reduced from 3.1 M to 2.1 M, significantly enhancing computational efficiency. Moreover, the model achieves an inference speed FPS of 86, maintaining high precision and meeting real-time detection requirements. This demonstrates that our method provides an efficient and reliable solution for license plate detection in surveillance scenarios.