Colorimetric Space Study: Application for Line Detection on Airport Areas

We propose an adaptive color reference refinement process for color detection in an aeronautical application: the detection of taxiway markings based on images acquired from an aircraft. Road markings detection is a key functionality for autonomous driving, and is actively studied in the literature. However, few studies have been conducted on aeronautics. Road markings are often detected by using color priors, sensitive to perturbations. Color-based algorithms are still favored in this context as the markings color provides important information. Our proposed method aims at reducing the impact of weather conditions, shadowing and illumination variations on color-based markings detection algorithms. Our approach adapts a given color reference in order to define a new flexible yet robust color reference while maximizing its difference to other colors in the image. It is achieved through a statistical analysis of color similarity over a set of images, computed on several color spaces and distance functions, in order to select the most relevant ones. We validate our approach by analyzing the quantitative improvement induced by this method using two color-based markings detection algorithms, based on the Hough Transform and the Particle Filter.