Characterizing the performance of automatic road detection using error propagation

A methodology is introduced to predict the performance of automatic road detection using image examples of typical road types. In contrast to previous work on road detection, the focus is on characterizing the detection performance to achieve reliable performance measures of the detection. It is studied how noise, like road markings, shadows, trees and buildings, influences the detection of road. This noise is modeled using second-order statistics and its effects are calculated using error propagation on the detection equations. The method predicts the performance in terms of detection rate and gives the optimal parameter set that is needed for this detection. Experiments have been conducted on a set of images of typical roads in very high-resolution satellite images.