基于卷积神经网络的边缘计算用于乘客计数:墨西哥瓜达拉哈拉的一个案例研究
Roxana Sánchez Laguna1, Ulises Davalos-Guzman1, Lina M Aguilar-Lobo2
1Maestría en Ciencias Computacionales, Universidad Autónoma de Guadalajara. Av. Patria 1201, Zapopan 45129, Mexico.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
这项研究引入了嵌入式乘客计数系统,以减少墨西哥公共交通等待时间. 通过准确跟踪乘客数量,该系统可以进行动态路线调整,以提高服务质量.
科学领域:
- 计算机科学 计算机科学
- 运输工程 运输工程
背景情况:
- 瓜达拉哈拉大都市区的公共交通面临着固定路线频率无法满足动态乘客需求的挑战,导致长时间的等待时间.
- 解决这个问题需要能够适应实时用户变化的智能系统.
研究的目的:
- 设计和实施一个嵌入式系统模块,用于在公共交通中准确的乘客计数.
- 通过对路线频率进行数据驱动的调整来提高公共交通服务质量.
- 为墨西哥的智能公共交通系统提供基础解决方案.
主要方法:
- 开发并实验验证了一种嵌入式乘客计数系统,使用图像分析来确定用户数量.
- 专门用于培训和测试墨西哥公共交通系统图像上的CSRNet算法,生成了两个新的数据集.
- 在Jetson Nano开发板上实现了乘客计数系统以实现实际应用.
主要成果:
- 设计的系统成功地从图像中计数乘客,并将这些数据传输到服务器.
- 创建了新的数据集,以提高CSRNet算法在墨西哥城市交通环境中的性能.
- 通过使用Jetson Nano实现了乘客计数系统的功能硬件实现.
结论:
- 开发的嵌入式系统为公共交通中的实时乘客计数提供了可行的解决方案.
- 这项技术可以为优化公共交通运营和提高用户体验做出重大贡献.
- 创建本地化数据集和硬件实施为墨西哥更智能的城市移动铺平了道路.
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