Infrared Small Target Detection Based on Saliency Guided Multi-Task Learning

Infrared (IR) small target detection is a challenging task due to the low contrast and low signal-to-noise ratio, generally yields high false alarm rates. To improve the performance of IR small target detection, we propose a saliency guided multi-task leaning model (SGMTLM). The model consists of two parts: feature fusion and saliency detection. The feature fusion module is to integrate shallow information and deep semantic information of small targets. The saliency detection module is used to guide the Feature Pyramid Networks (FPN) to focus on the small target area. It can effectively suppress the non-target information while enhancing the small target information. Finally, experimental results on two datasets Small-ExtIRShip and Small-SSDD demonstrated that, with the help of saliency detection, the proposed method can effectively improve the accuracy of IR small target detection, achieving 95.78% and 98.70% mAP on the two datasets, respectively.