Road detection system based on RGB histogram filterization and boundary classifier

Abstract

The purpose of this paper is to describe a new approach in road detection. This research uses two detection processes approaches: RGB histogram Filterization and Boundary Classifer, which is different from previous works on road detection. RGB Histogram Filterization processes the reading from the camera in greyscale form and afterward processes them by color segmentation. The last step for this process is determining area between the slopes, which is considered to be the road area. Boundary classification process then employs the RGB indexing on slope ranges, and mapping them into real pictures of roads and its environments. The next process is specifically looking for line boundaries by using Hough-Transform and Canny Edge Detection, and transforms them into binary numbers of `0' and `1'. `1' represents road boundaries while `0' represents surrounding area. The coordinate of `1', then mapped by cubic spline to produce connecting line between point `1' coordinates, which in the end produce sharp images on boundaries between road and non-road. This model has proven to be able to detect road conditions and distinguish roads from non-road in a precise way. A test is already conducted for the system by using real-time roads in Bandung, Indonesia. The results are really promising for the road condition on both straight and curved road area.