Liu Xinchao, Hong Liang, Lin Yier
College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, China.
Sensors (Basel). 2023 Aug 29;23(17):7506. doi: 10.3390/s23177506.
In a foggy traffic environment, the vision sensor signal of intelligent vehicles will be distorted, the outline of obstacles will become blurred, and the color information in the traffic road will be missing. To solve this problem, four ultra-fast defogging strategies in a traffic environment are proposed for the first time. Through experiments, it is found that the performance of Fast Defogging Strategy 3 is more suitable for fast defogging in a traffic environment. This strategy reduces the original foggy picture by 256 times via bilinear interpolation, and the defogging is processed via the dark channel prior algorithm. Then, the image after fog removal is processed via 4-time upsampling and Gaussian transform. Compared with the original dark channel prior algorithm, the image edge is clearer, and the color information is enhanced. The fast defogging strategy and the original dark channel prior algorithm can reduce the defogging time by 83.93-84.92%. Then, the image after fog removal is inputted into the YOLOv4, YOLOv5, YOLOv6, and YOLOv7 target detection algorithms for detection and verification. It is proven that the image after fog removal can effectively detect vehicles and pedestrians in a complex traffic environment. The experimental results show that the fast defogging strategy is suitable for fast defogging in a traffic environment.
在有雾的交通环境中，智能车辆的视觉传感器信号会失真，障碍物的轮廓会变得模糊，交通道路中的颜色信息也会缺失。为了解决这个问题，首次提出了四种交通环境中的超快速去雾策略。通过实验发现，快速去雾策略3的性能更适合交通环境中的快速去雾。该策略通过双线性插值将原始有雾图像缩小256倍，然后通过暗通道先验算法进行去雾处理。接着，对去雾后的图像进行4次上采样和高斯变换。与原始暗通道先验算法相比，图像边缘更清晰，颜色信息得到增强。快速去雾策略和原始暗通道先验算法可将去雾时间减少83.93-84.92%。然后，将去雾后的图像输入到YOLOv4、YOLOv5、YOLOv6和YOLOv7目标检测算法中进行检测和验证。结果证明，去雾后的图像能够在复杂交通环境中有效地检测车辆和行人。实验结果表明，快速去雾策略适用于交通环境中的快速去雾。
