实时道路场景分类和增强在恶劣天气下驾驶员辅助的道路场景
1School of Mechanical Engineering, Vellore Institute of Technology, Chennai, India.
Scientific reports
|November 20, 2025
概括
这项研究引入了一种机器学习系统,用于分类道路状况,如雾或雨. 它提高了驾驶安全的可见性,在廉价的Raspberry Pi系统上达到98.67%的准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 汽车安全 汽车安全
背景情况:
- 高速公路运输严重依赖于驾驶员的可见性,在恶劣的天气条件下,驾驶员的可见性往往受到损害.
- 目前的系统缺乏强大的解决方案,可以在各种各样的大气场景中实时增强可见性.
研究的目的:
- 开发一种高效的基于机器学习的系统,用于分类各种道路场景 (白天,夜晚,雾,雨).
- 应用特定场景的图像增强技术来提高道路可见性.
- 为实际应用创建一个负担得起的,低成本的硬件实现.
主要方法:
- 机器学习算法被评估为道路场景分类准确性.
- 随机委员会算法因其卓越的性能而被选中.
- 应用了特定场景的增强技术:对光进行高强度映射,对夜晚的低光增强,以及对恶劣天气的脱雾/脱水算法.
- 使用Raspberry Pi 5,USB摄像头和7英寸显示器实现了一个系统.
主要成果:
- 拟议的模型实现了98.67%的分类准确度,超过了像Resnet-101和定制CNN这样的最先进方法.
- 与原始图像相比,增强图像显示了基于YOLO的对象检测性能提高.
- 该系统即使在有限的硬件上也被证明是有效的.
结论:
- 开发的系统提供了一个高度准确和高效的解决方案,用于在各种条件下提高道路可见性.
- 这种方法显示了集成到高级驾驶辅助系统 (ADAS) 和自动驾驶汽车的巨大潜力.
- 该方法验证了基于机器学习的图像增强在改善汽车安全和感知方面的有效性.
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