The invention belongs to the technical field of
image processing and unmanned aerial
vehicle detection, and relates to a method for detecting a small unmanned aerial vehicle target based on super-pixels and scene prediction. The method mainly comprises the steps of preprocessing, unmanned aerial vehicle target
probability estimation and unmanned aerial
vehicle detection, and is characterized in that in the step of preprocessing, super-pixel generation and scene classification are performed on an
optical image to be detected so as to acquire a super-
pixel based scene classification image; in the step of unmanned aerial vehicle target
probability estimation, a significance depth value of each scene in the classification image acquired in the step a is respectively estimated, and the probability of existence of an unmanned aerial vehicle of each scene is calculated; and in the step of unmanned aerial
vehicle detection, feature of the image to be detected are extracted,
feature saliency maps are acquired by adopting an SVD based multilayer
pyramid structure, weighting is performed on the different
feature saliency maps to acquire a general
saliency map, the general
saliency map is loaded into the super-
pixel classification image acquired in the step a, and a target detection result of an unmanned aerial vehicle is acquired according to a weight of the probability acquired in the step b when being applied to different scene areas by adopting a mechanism of winner-take-all and return inhibition. The method has the beneficial effect that the detection accuracy is higher compared with traditional technologies.