Based on the visibility detection principle

this paper aims to enhance the accuracy for highway visibility detection in foggy weather through improving the existing dark channel prior algorithm by considering the atmospheric transmittance intensity

transmittance

atmospheric extinction coefficient

and the actual distance from a point in the image to the camera. Firstly

the problem of reconstructing 2D scene to 3D scene on the highway was solved by combining the rectangular area ranging and the actual scene object size. The K-means clustering algorithm was used to obtain the point with the minimum depth of field on the dividing line of the video image. The actual distance from the point to the camera was obtained by using the constructed ranging model. Secondly

in view of the deficiency of the traditional dark channel prior theory in solving the atmospheric transmittance intensity

a local entropy method based on image segmentation was proposed to obtain the atmospheric transmittance intensity. Then

the transmittance was obtained by using the dark channel prior theory

and the visibility was calculated by the visibility detection principle. Finally

according to the video image of K113+000 of Rilan Highway in foggy weather

the improved dark channel prior algorithm was compared with the traditional dark channel prior algorithm experimentally

and the results were referenced to the detection results of visibility detector. Results showed that when the actual visibility was about 100 m

the mean relative error (MRE) detected by the improved algorithm was 6.25%

which was 2.38% less than that of the traditional algorithm. When the actual visibility was about 150 m

the MRE detected by the improved algorithm was 6.17%

which was 3.06% less than that of the traditional algorithm. When the actual visibility was about 200 m

the MRE detected by the improved algorithm was 5.71%

which was 3.41% less than that of the traditional algorithm. With the increase in light intensity

the improved dark channel prior method has higher detection accuracy and can better adapt to the highway in daytime foggy weather.