A horizon detection algorithm based on between-class variance analysis

To detect the horizon for the steady flight control of micro air vehicles (MAV) in vision navigation, a horizon detection algorithm based on between-class variance analysis is proposed. The angle and distance are used as parameters to exhaust the straight lines in the image. The blue color component of the pixel is chosen as the feature for distinguishing the sky and the ground. A criterion based on the between-class variance is set up for determining whether or not a straight line is the horizon within the exhaustive straight lines. Experimental results show that, compared with the horizon detection algorithm based on color covariance matrix calculation, the proposed algorithm performs better. The average detection error decreases by 1 degree in angle and 3 pixels in distance. The correct detection rate increases by 1.98% in angle and 4.95% in distance at a satisfactory speed.