Computational Fluid Dynamic Network for Infrared Small Target Detection.

Abstract

Infrared small target detection (IRSTD) aims to identify and locate small targets amidst background noise. It is highly valuable in various practical application domains, such as maritime rescue and early warning systems deployed in challenging conditions such as harsh weather, low illumination, and long imaging distances. Different from existing works that either adopt well-designed backbone networks or devise specific modules to improve them from different aspects, in this article, we formulate the learning process of IRSTD from a novel perspective, i.e., the mechanism of pixel movement. Considering that the movement of pixels passing through the layers of the network for IRSTD can be analogized to the flow of particles in a fluid dynamic system, we propose a computational fluid dynamic network (CFD-Net) derived from computational fluid dynamics. Technically, we leverage the superiority of the unilateral difference equation with third-order accuracy and devise a unilateral differential residual structure as the backbone of CFD-Net. This design ensures that the pixel stream only flows in the forward direction. In addition, a switch-controlled multidirectional treatment tank (SMTT) is introduced to CFD-Net to dynamically guide the pixel stream to the appropriate path for different targets with varying shapes and orientations, facilitating learning robust target representation and improving detection performance. The proposed CFD-Net is evaluated on the IRSTD-1k and SIRST datasets and is found to outperform existing state-of-the-art (SOTA) methods.