EAA-Net: Efficient Anti-Aliasing Network for Single-Frame Infrared Small Target Detection

Single-frame Infrared Small Target Detection (SIRSTD) identifies and isolates small objects in a single infrared image, playing a crucial role in fields such as search and rescue, particularly under extreme climatic conditions. The main challenge lies in segmenting the small target from the background. When the sampling frequency doesn’t meet the Nyquist criterion, aliasing occurs, which significantly reduces the accuracy of detection. Although deep learning has significantly improved the accuracy and efficiency of SIRSTD, it also brings issues such as high memory usage, redundant parameters, and decreased efficiency. To address these challenges, we propose a novel efficient anti-aliasing network (EAA-Net) for single-frame infrared small target detection. Our approach involves an anti-aliasing contextual aggregation (AACA) module and a partial differential channel pruning (PDCP) module. The AACA module suppresses aliasing effects by filtering out channels severely affected by aliasing. In addition, to tackle the problems of redundant parameters and low model efficiency, we design a PDCP module which represents the weight matrix with the partial differential equation (PDE) and formulate a partial differential channel pruning method. These structures not only significantly enhance the capacity of anti-aliasing of the network but also reduce the size. Extensive experiments on the public IRSTD-1k and SIRST datasets demonstrate that the proposed EAA-Net outperforms the state-of-the-art methods.