通过图像质量提升和特征蒸来传输增强的材料知识,以识别路面状况
Zejiu Wu1, Yuxing Zou2, Boyang Liu2
1School of Science, East China Jiaotong University, Nanchang, 330013, China. wzjhdjd@qq.com.
Scientific reports
|April 21, 2025
概括
一个新的图像质量提升和特征蒸 (IQEFD) 模型准确地识别了路面条件,如干燥,湿和积雪,即使在挑战性的夜间驾驶场景中也适用于自动驾驶汽车.
科学领域:
- 计算机视觉 计算机视觉
- 自主驾驶系统 自主驾驶系统
- 机器学习 机器学习
背景情况:
- 自动驾驶技术需要强大的感知系统,以确保安全和舒适.
- 准确识别路面状况至关重要,特别是在恶劣的天气和照明条件下.
研究的目的:
- 提出一个新的框架,IQEFD,以检测昼夜不同的路面状况.
- 为了提高自动驾驶汽车导航的状态分类准确性.
主要方法:
- 使用ConvNeXt骨干来进行高质量的特征提取.
- 采用双向融合模块与混合注意力机制 (HAM) 进行多级特征改进.
- 集成图像增强 (Zero-DCE) 和特征蒸,以改善表示对齐.
主要成果:
- 在公开数据集上,IQEFD在干燥,潮湿和积雪条件下实现了高精度 (98.04%和98.68%).
- 在杂的夜间图像上表现出强大的性能.
- 在MattrSet数据集上显示了概括能力,平均准确率为75.86%.
结论:
- IQEFD框架为路面状况识别提供了一种新且有效的方法.
- 该车型的性能突显了其提高自动驾驶安全性和可靠性的潜力.
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