https://github.com/zhengpeng7/detect_visual_limits

detect the visual limits on the traffic roads
https://github.com/zhengpeng7/detect_visual_limits

fog image-processing matlab visibility-detection

Last synced: 8 months ago
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detect the visual limits on the traffic roads

• Host: GitHub
• URL: https://github.com/zhengpeng7/detect_visual_limits
• Owner: ZhengPeng7
• License: mit
• Created: 2018-03-28T12:19:10.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2018-05-24T06:37:55.000Z (about 8 years ago)
• Last Synced: 2025-08-15T19:43:45.719Z (10 months ago)
• Topics: fog, image-processing, matlab, visibility-detection
• Language: Matlab
• Homepage:
• Size: 970 KB
• Stars: 4
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Detect visual limits of the road in foggy scene.

1. Method 1: in the directory "visibility_detection_by_contrast", the contrast method is used. The code took the 4-nearest pixels to calculate the contrast value(In the paper[1], it's said that use 2-nearest pixels can get the same effect while saving much time) -- ___if the contrast value of the pixel reaches the threshold and it's not a very isolated one, its y_axis value is perhaps the visual limit___. The paper referred is below.

2. Method 2: in the directory "visibility_detection_by_2nd_derivative_only_road_surface", referred to paper[2]the 2nd derivative method is used as: 

   1. ___Get the vector containing the mean of gray values of each line in a road scene image.___

   2. ___Calculate the 2nd derivative of the gray values, and find where the 2nd derivative equals 0, of which the y_axis value is probably the visual limit. Besides, I restricted the visual limits in the road region, to get rid of the interference of the trees or other things around.___

   3. ___However, the candidate y may exist a lot, so the code here took the result of method 1 -- select the y which is nearest to the result in method 1.___

   4. ___It seems that method 2 is better than method 1 here, while referred on results of method 1.___

### Results:

- method 1:

![method_1_res](https://raw.githubusercontent.com/ZhengPeng7/detect_visual_limits/master/visibility_detection_contrast/results/1_point_05/1.jpg)

- method 2:

![method_2_res](https://raw.githubusercontent.com/ZhengPeng7/detect_visual_limits/master/visibility_detection_by_2nd_derivative_only_road_surface/results/1_point_05/1.jpg)

> [Paper1](http://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFD2009&filename=JSJF200911010&v=MTA5Mjl0ak5ybzlFWklSOGVYMUx1eFlTN0RoMVQzcVRyV00xRnJDVVJMS2ZaT1JuRkNua1c3eklMejdCYUxHNEg=) -- 李勃,董蓉,陈启美.无需人工标记的视频对比度道路能见度检测[J].计算机辅助设计与图形学学报,2009,21(11):1575-1582.

>

> [Paper2](https://www.researchgate.net/publication/220464605_Automatic_fog_detection_and_estimation_of_visibility_distance_through_use_of_an_onboard_camera) -- Hautière, Nicolas & Tarel, Jean-Philippe & Lavenant, Jean & Aubert, Didier. (2006). Automatic fog detection and estimation of visibility distance through use of an onboard camera. Mach. Vis. Appl.. 17. 8-20. 10.1007/s00138-005-0011-1.