在基于OCC的车辆网络中使用图像重建进行强大的车牌识别
Dingfa Zhang1, Ziwei Liu1, Weiye Zhu1
1College of Computer Science, Sichuan University, Chengdu 610065, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
光学摄像头通信 (OCC) 干扰了车牌识别. 我们开发了一种新方法,可以从图像中删除OCC条纹,大大提高了车辆网络的识别精度.
科学领域:
- 计算机视觉 计算机视觉
- 汽车的互联网 (IoV)
- 光学摄像机通讯 (OCC) 技术
背景情况:
- 光学摄像头通信 (OCC) 通过使用交通信号灯和街头摄像头实现车辆与基础设施之间的通信.
- OCC光辐射会在图像中产生条纹图案,降低车牌识别系统的准确性.
- 现有的车牌识别系统在汽车互联网 (IoV) 应用中与OCC的干扰作斗争.
研究的目的:
- 提出和演示一种方法来减少OCC条纹干扰在车牌识别.
- 为了提高在使用光学摄像头通信的环境中识别车牌的准确性.
主要方法:
- 开发了一种具有端到端图像重建模块的创新管道.
- 模拟的OCC条纹作为乘法噪声,从现有的车牌数据中合成一对数据集.
- 构建了一个原型来模拟现实世界的OCC-based车辆网络场景并收集数据.
主要成果:
- 在合成数据集上实现了81.58%的识别性能,在现实场景中捕获的数据上达到79.35%.
- 与传统方法相比,显示出大约31.18%和24.26%的显著改善.
- 提出的方法有效地重建高质量的车牌图像,在OCC条件下进行识别.
结论:
- 开发的方法成功地减轻了OCC条纹干扰,提高了车牌识别准确度.
- 该方法为支持OCC的IoV系统中可靠的车辆识别提供了可行的解决方案.
- 这项研究有助于在智能交通系统中实际部署光学摄像头通信.
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