||The monitoring video camera systems have been widely applied in our daily life. Typical examples include the surveillance system at intersections, the anti-theft CCTV security system, the drivers’ assistance system, and the event data recorder, which already became the most popular device installed on current vehicles. Machine vision is apparently capable of providing supplementary image information for versatile applications in real world. As a result, researches on this specific field of machine vision have been growing rapidly.|
This thesis focuses on the implementation of machine vision for drivers in rainy days. Based on the event data recorder inside the vehicle, develop image processing techniques to deal with the blurred image caused by raindrops on windshield and to restore the image to a clearer status. This achievement provides a more efficient way for event recording and evidence collection, and can also be extended to real-time applications for assisting driving vehicles.
Experiments were carried out by adopting methods for raindrop detection presented in literatures. The techniques including the threshold filter, the gradient detection, and morphological processing were applied to acquire information of raindrops. Experimental results indicate that residual noises and some forms of image boundary can bring about false positives. In this thesis, therefore, parameters modification and morphological preprocessing are added. Comparing with the original process, each approach has its own advantages. Complementary performance can be obtained by combining those two processes. Finally, removal of detected raindrops is followed by image recovery according to background pixels so that a better image can be provided for the drivers.