一个基于混合边缘云计算的轻型车辆牌照识别系统
Jiancai Leng1, Xinyi Chen1, Jinzhao Zhao1
1International School of Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Changqing District, Jinan 250300, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
这项研究引入了一个Edge-LPR系统,用于高效地识别新能源汽车的车牌. 它通过在边缘设备上部署轻量级模型来减少延迟和能源消耗,达到97%的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 可持续发展 可持续发展 可持续发展
背景情况:
- 全球新能源汽车正在快速增长,对高效系统的需求正在增加.
- 目前基于深度学习的车牌识别 (LPR) 系统存在高延迟和能源消耗问题.
- 对LPR云计算的过度依赖在资源分配和实施方面带来了挑战.
研究的目的:
- 提出一个创新的Edge-LPR系统,以便及时,有效,节能地识别车牌.
- 通过利用边缘计算来缓解基于云的LPR系统的局限性.
- 优化LPR模型以减少边缘设备上的资源消耗.
主要方法:
- 开发了一个使用边缘计算和轻量级网络模型的Edge-LPR系统.
- 采用通道修剪来重建骨干层并减少网络模型参数.
- 在边缘网关上部署网络模型,使用英特尔的第二代计算棒直接检测车牌.
主要成果:
- 边缘-LPR系统显著减少对云计算资源的依赖.
- 优化的网络模型实现了只有0.606 MB的总参数数.
- 在CCPD数据集上的实验验证显示了97%的高准确率.
结论:
- 边缘LPR系统为新能源汽车应用中的车牌识别提供了可靠和有效的解决方案.
- 与轻量级模型的边缘计算集成为传统LPR系统提供了节能且低延迟的替代方案.
- 拟议的系统证明了在充电站等环境中实时监控的实际可行性.
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