基于改进的YOLOv5s和LPRNet的复杂场景的车牌识别系统.
Xuanhong Wang1, Mingchen Wang1, Hongyu Guo1
1School of Communication and Information Engineering, Xi'an University of Posts and Telecomunications, Xi'an, 710121, China.
Scientific reports
|October 6, 2025
概括
本研究引入了使用YOLOv5s和LPRNet改进的车牌识别 (LPR) 系统,提高了复杂条件下的准确性,并使其能够在移动设备上部署.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 传统的车牌识别 (LPR) 算法在恶劣的天气和车牌倾斜等具有挑战性的环境中难以准确.
- 由于硬件限制,部署LPR系统往往是困难的.
研究的目的:
- 开发一种轻量级,端到端的方法来检测和识别车牌.
- 在复杂的场景中提高LPR的稳定性和准确性.
- 为了使LPR在资源有限的平台上部署.
主要方法:
- 集成了一个改进的YOLOv5s与LPRNet,结合三重注意力来增强功能提取.
- 推出软NMS,可对重叠的车牌进行强有力的检测.
- 实施了空间变压器网络 (STN) 用于对倾斜的车牌进行几何校正.
主要成果:
- 在CCPD2019数据集上实现了98.9%的检测精度和91.5%的识别精度.
- 与基线YOLOv5s + LPRNet.相比,已经显示出显著的改善.
- 该模型拥有7.5M参数和18.1 GFLOPs,以147 FPS进行检测.
结论:
- 拟议的轻量级LPR方法显著提高了复杂条件下的检测和识别精度.
- 该系统的效率和低资源需求使其适用于移动和嵌入式应用程序.
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