Avian contrast sensitivity inspired contour detector for unmanned aerial vehicle landing

Abstract

Runway detection is a demanding task for autonomous landing of unmanned aerial vehicles. Inspired by the attenuation effect and surround suppression mechanism, a novel biologically computational method based on the avian contrast sensitivity is proposed for runway contour detection. For the noisy stimuli, deniosed responses of the biologically inspired Gabor energy operator are generalized followed by the denoising layer and the multiresolution fusion layer. Moreover, two factors such as contour effect and texture suppression are considered in the contrast sensitivity based surround inhibition. Different from traditional detectors, which do not distinguish between contours and texture edges, the proposed method can respond strongly to contours and suppress the texture information. Applying the contrast sensitivity inspired detector to noisy runway scenes yields effective contours, while the non-meaningful texture elements are removed dramatically at the same time. Besides the superior performance over traditional detectors, the proposed method is capable to provide insight into the attenuation effect of the avian contrast sensitivity function and has potential applications in computer vision and pattern recognition.