在反向摄像头中检测行人使用多模式卷积神经网络
Luis C Reveles-Gómez1, Huizilopoztli Luna-García1, José M Celaya-Padilla1
1Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究介绍了用于车辆安全的先进人工智能 (AI) 模型. 人工智能系统使用摄像头和传感器数据准确地检测车辆后面的行人,提高道路安全.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 汽车工程 汽车工程
背景情况:
- 道路安全是全球关注的问题,行人检测系统对于减少事故至关重要.
- 目前的系统往往专注于向前检测,忽视了与车辆倒车相关的风险.
- 反向行驶时与行人发生碰撞,构成了严重的安全隐患.
研究的目的:
- 开发和评估一种人工智能模型,用于在倒车时检测车辆后面的行人.
- 将备份摄像头和超声波传感器的数据融合在一起,以提高检测准确度.
- 为开发智能汽车安全系统做出贡献.
主要方法:
- 提出了一个结合一维卷积神经网络 (CNN) 和Inception V3架构的新型模型.
- 来自车辆备用摄像头和超声波传感器的信息被融合在一起.
- 通过专门收集用于培训和验证的数据,创建了一个专门的数据库.
主要成果:
- 拟议的CNN模型在行人检测方面实现了高性能.
- 该模型显示了99.85%的准确性和99.86%的正确分类.
- 摄像机和传感器数据的融合证明了对强大的检测有效.
结论:
- 这项研究成功地证明了CNN在反向驾驶时检测行人方面的有效性.
- 来自多个传感器的数据融合显著提高了行人检测系统的可靠性.
- 开发的模型为提高汽车安全和预防事故提供了可行的解决方案.
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