Detection and tracking of infrared small target by jointly using SSD and pipeline filter

Infrared imaging has been an efficient anti-drone approach due to its low-cost, anti-interference and all-weather working characteristics. However, the detection of Unmanned Aerial Vehicle (UAV) through infrared camera is still a challenging issue because infrared targets in the field-of-view are usually small and lack of shape and texture features. In this paper, we propose an infrared small target detection and tracking method based on deep learning. We improve the network architecture of Single Shot MultiBox Detector (SSD) for infrared small target detection, called Single Shot MultiBox Detector for Small Target (SSD-ST), by dropping low-resolution layers and enhance high-resolution layer. In addition, in order to further reduce the false alarm rate and improve the precision, we also design an Adaptive Pipeline Filter (APF) based on the temporal correlation and motion information to correct the detection results. We have evaluated our method over a dataset with 16177 infrared images and 30 trajectories. The results show our method is more robust than traditional methods in complex scenes, and achieve a recall rate higher than 90% and a precision higher than 95%, which prove that our method can well complete the detection and tracking task of infrared small targets.