Deep learning and optimization algorithm for high efficient searching and detection of aircraft targets in remote sensing images

In order to achieve high-performance detection and accurate positioning of aircraft targets in large-scale remote sensing images, in this paper, a kind of efficient aircraft target detection algorithm based on synthetic integration of searching and detection is presented. First, through the end-to-end deep neural networks (DNN), the efficient and accurate pixel-level segmentation of apron and runway area is achieved so that the searching range of aircraft targets is greatly narrowed and the probability of false alarm is also reduced effectively and the goal of high speed searching of aircraft targets candidate detection areas is achieved accordingly. In view of the segmented areas of apron and runway, the strategy of transfer reinforcement learning is employed to pre-trained YOLO networks with supervised signals of positive datasets by manual labelling. In this way, pre-trained networks can make up the deficiency of capacity of manual data sets, and the advantage of real-time property of YOLO networks can also be utilized to deal with the classification and locations of aircraft targets so as to achieve a satisfied solution of regression problems and promote the efficiency of detection significantly. It is obvious that the apron and runway segmentation with DNN networks can play important role in getting performance superiority of high precision and efficiency. Meanwhile, YOLO networks based on transfer reinforcement learning not only possess the characteristics of high efficiency, but also maintain the precision of detection at a high level. A series of comprehensive experiments and comparative analyses verify the effectiveness and good performance of the proposed searching and detection algorithm of aircraft targets with DNN cascade combination and synthetic integration.