Road safety is always an area that concerned many people around the world and systems that aid the drivers have been appearing ever since cars and computers were combined to make driving safer and more efficient. There are plenty of systems that are able to warn drivers about different types of dangers: lane departure, collision possibility and various traffic signs. However, there is still room for development, because modern technologies, like the rising vision about the OPEN-CV, allow us to create much more efficient systems. Also, the detections can be improved to perform better in various situations, such as different light conditions, road quality, etc. In this project, we present the plans of a driver-assistance system, which will be capable of road lane and traffic sign detection by using an OPEN-CV.
Lane coloration has become popular in real time vehicular ad-hoc networks (VANETs). The main emphasis of this paper is to find the further ways which can be used further to improve the result of lane detection algorithms. Noise, visibility etc. can reduce the performance or the existing lane detection algorithms. The methods developed so far are working efficiently and giving good results in case when noise is not present in the images. But problem is that they fail or not give efficient results when there is any kind of noise or fog in the road images. The noise can be anything like dust, shadows, puddles, oil stains, tire skid marks, etc.
It is developed thorugh OpenCV (Deep Learning) Programming : Python
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