Development of Edge-Based Lane Detection Algorithm using Image Processing

Abstract

Image processing is known as the process of converting an image into a digital form so as to obtain an enhanced image and to extract useful information from it. This paper presents a simple step by step analysis of edges-based lane detection. Some of the known and common edge detection techniques such as Sobel, Canny, Prewitt and Roberts were studied and evaluated using image segmentation, morphology, image statistic and Hough Transform. The result indicated some similarities in the process as well as major differences. These differences were observed to be as a result of the high sensitivity of the edge detector in detecting noise such as cast shadows and unmarked lanes. This could be noticed in the case of canny edge detector. Also these data could be considered in the development of a multi-system edge detector, which could be used to analyze various road scenes and runs the appropriate edge detector best suited for the current situation.