雾道路场景的图像预测算法基于改进的变压器
Bo-Tao Zhang1, Ai-Ying Zhao2, Pei Xiong3
1College of Science, Shihezi University, Shihezi, 832003, China. 20221006184@stu.shzu.edu.cn.
Scientific reports
|March 1, 2026
概括
本研究介绍了一种基于变压器的雾状图像预测算法,以提高在严重雾中自动驾驶的安全性. 该方法通过在雾条件下有效预测道路场景来提高可见性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 自主驾驶系统 自主驾驶系统
背景情况:
- 严重的雾天气显著降低驾驶可见度,对道路安全构成重大风险.
- 当前的自动驾驶系统在恶劣的天气条件下 (如重雾) 难以进行感知和预测.
研究的目的:
- 建议使用变压器架构对道路场景进行雾状图像预测算法.
- 提高在严重雾天气中自动驾驶系统的视觉感知和预测能力.
主要方法:
- 利用一个变压器模型与泰勒扩展的多头自我注意力来降低计算成本.
- 实现了多分支架构,具有多尺度补丁嵌入,具有可变形卷积用于特征提取.
- 在道路场景中开发了一种用于雾图像预测的算法.
主要成果:
- 在定制的雾数据集上,达到12.9836的峰值信号噪声比 (PSNR) 和0.6278的结构相似度指数 (SSIM).
- 在模糊的天气条件下有效预测真实图像.
- 展示了良好的图像预测结果,计算性能要求相对较低.
结论:
- 拟议的基于变压器的方法有效地改善了雾条件下的可见性,解决了关键的驾驶安全问题.
- 该算法通过在恶劣天气中提高性能,为自动驾驶技术的发展做出了贡献.
- 该方法为在雾环境中实时图像预测和感知提供了可行的解决方案.
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