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相关实验视频

Updated: Jun 13, 2025

Determination of the Friction Coefficients of Icy Pavements Under Different Amounts of Snowfall
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改进了基于MobileNet V3的道路附着系数识别方法.

Binglin Li1, Jianqiang Xu1, Yufeng Lian1

  • 1School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China.

Sensors (Basel, Switzerland)
|September 14, 2024
PubMed
概括

本研究介绍了一种改进的MobileNet V3模型,用于预测道路表面状况和识别道路附着系数,增强车辆安全系统. 这种新方法实现了95.53%的分类精度,超过了现有技术.

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科学领域:

  • 汽车工程 汽车工程
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 汽车主动安全系统需要针对复杂条件及时调整控制策略.
  • 准确的路面状况和粘附系数识别对于提高驾驶安全至关重要.

研究的目的:

  • 开发一个增强的MobileNet V3模型,用于预测道路表面信息和识别道路粘附系数.
  • 提高汽车主动安全系统的适应性和安全性.

主要方法:

  • 该研究改进了MobileNet V3模型,将Squeeze-and-Excitation (SE) 模块替换为Convolutional Block Attention Module (CBAM),以改善特征提取.
  • 交叉损失函数被取代为偏差损失函数,以减轻随机预测问题并提高识别精度.
  • 拟议的方法使用四轮驱动ROS机器人平台进行了验证.

主要成果:

  • 改进的MobileNet V3模型在识别道路粘附系数方面实现了95.53%的分类精度.
  • 通过考虑空间和通道维度,CBAM模块有效地改善了特征提取.
  • 偏差损失函数减少了预测错误,从而提高了识别准确度.

结论:

关键词:
偏差 损失函数 偏差 损失函数卷积块注意力模块 卷积块注意力模块移动网络 V3 移动网络 V3机器人平台 ROS 机器人平台道路粘附系数识别方法

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  • 与现有方法相比,拟议的方法显著提高了道路粘附系数识别的准确性.
  • 改进后的模型为在主动安全系统中实时预测路面状况提供了一个有前途的解决方案.
  • 这项研究有助于推进智能汽车安全技术的发展.