交通环境的快速清除雾策略
Xinchao Liu1, Liang Hong1, Yier Lin1
1College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究介绍了智能车辆的快速除雾策略,改善了雾中的视力. 策略3提高图像清晰度和颜色,使有效的车辆和行人检测.
科学领域:
- 计算机视觉 计算机视觉
- 智能运输系统 智能运输系统
- 图像处理 图像处理
背景情况:
- 雾状况会扭曲智能车辆视觉传感器,模糊障碍物并失去颜色信息.
- 有效的实时图像处理对于在恶劣天气下自动驾驶的安全至关重要.
研究的目的:
- 开发和评估在交通环境中运行的智能车辆的超快速脱雾策略.
- 为了提高对象检测算法在雾条件下的性能.
主要方法:
- 提出了四种超快速脱雾策略,其中策略3被认为是最合适的.
- 实施了使用双线插值,暗通道先行算法,上采样和高斯变换的战略3.
- 通过使用YOLOv4-v7对象检测算法验证了除模糊的图像.
主要成果:
- 与原来的黑暗通道之前的算法相比,快速除雾策略3显著改善了图像边缘清晰度和颜色信息.
- 使用建议的快速策略,除雾时间减少了83.93-84.92%.
- 清除雾的图像使YOLO算法能够有效地检测车辆和行人.
结论:
- 建议的快速脱雾策略对于实时交通环境应用是有效的.
- 在雾条件下提高图像质量,提高智能汽车感知系统的可靠性.
- 这项研究有助于在恶劣天气中实现更安全的自主导航.
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