Robust Aircraft Detection with a Simple and Efficient Model

Abstract

Aircraft detection is the main task of the optoelectronic guiding and monitoring system in airports. In practical applications, we demand not only detection accuracy, but also efficiency. Existing detection approaches always train a set of holistic templates to search over a multi-scale image space, which is inefficient and costly. Moreover, the holistic templates are sensitive to the occluded or truncated object, although they are trained by many complicated features. To address these problems, we firstly propose a kind of local informative feature which combines a local image patch with its corresponding location. Additionally, for computational reasons, a feature compression method (based on sparse representation and compressive sensing) is proposed to reduce the dimensionality of the feature vector, and which shows excellent performance. Thirdly, to improve the detection accuracy during detection stage, a position estimation algorithm is proposed to calibrate the aircraft’s centroid. From the experimental results, our model achieves favorable detection accuracy, especially for the partially-occluded object. Furthermore, the detection speed is remarkably improved as well.