A Lightweight Two-Level Nested FPN Network for Infrared Small Target Detection

Infrared small target detection technology has attracted great attention and made significant research progress in military and civilian fields. However, most existing infrared small target detection algorithms fail to balance high detection accuracy and low computational complexity. To address this issue, we propose a simple and effective deep network framework for infrared small target detection. Our two-layer nested FPN (TNFPN) architecture is a lightweight TNFPN structure. Specifically, each layer or stage in our feature pyramid networks (FPN) network embeds a small residual FPN (ResFPN) network. The designed ResFPN block can capture richer contextual information from different scales, thereby learning finer feature representations. Meanwhile, the use of downsampling in ResFPN blocks enhances the depth of the structure without significantly increasing the computational complexity of the overall structure. In addition, we design an asymmetric attention interaction module (AAIM), which filters out necessary semantic information contained in low-level features using high-level features as weights, thus eliminating the negative impact of background noise on overall detection performance. We conduct experiments on three publicly available infrared small target datasets, and the results show that our proposed TNFPN, with only 1.85 M parameters, achieves high detection accuracy while maintaining an inference speed of 41.43 FPS. The code is uploaded to: github.com/WoodXXX/TNFPN.