A Novel Infrared Small Target Detection Algorithm Based on Deep Learning

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 a novel deep learning-based infrared small target detection algorithm called Single Shot Detector for Small Target (SSD-ST) by redesigning the network architecture of Single Shot Detector (SSD). We drop the low-resolution layers and enhance the high-resolution layer in SSD to make it more suitable for the detection task of infrared small targets. We have evaluated our algorithm over a dataset with 16177 infrared images and 30 trajectories. The results show that the proposed algorithm can effectively reduce the false detection rate and achieve higher than 90% precision and recall rate on the infrared small target dataset.