A Synthetic Integrative Algorithm of Fast Concurrent Detection-Recognition with Effective Tracking of Aerial Objects based on Deep Learning of CNN

In this paper, a synthetic integrative algorithm of fast concurrent detection-recognition with effective tracking of aerial objects is presented, in which the deep learning of CNN is employed to carry out high-performance detection, recognition and tracking of aerial objects. In the synthetic integrative algorithm, a concurrent detection-recognition mechanism is designed and implemented based on DarkNet-53 with FPN structure and RFB module. Furthermore, the object movement is predicted by well-trained Siamese RPN with image transformation so as to achieve a high-performance tracking for aerial objects. In view of the shortcoming of missing target in the tracking stage and some false positive results in the stage of concurrent detection-recognition a discriminative classifier is designed to deal with and eliminate the false positive ones. Finally, a synthetic optimization strategy is proposed towards the integration of concurrent detection-recognition with tracking by filtering out disturbances with the discriminative classifier and confirming effective results by the IOU matching so as to make the whole algorithm works well and achieve a superior performance. Based on the simulation demonstration with a software of X-Plane 10, two datasets, Aerial Object Detection (AOD) and Aerial Object Tracking (AOT), are generated, and a series of experiments are conducted on the AOD and AOT to verify and validate the performance advantages of our proposed algorithms.