Cross-Layer Feature Guided Multiscale Infrared Small Target Detection

Abstract

Infrared small target detection (ISTD) has a wide range of applications in both military and civilian fields. Due to their low contrast and the absence of color and texture information, small targets in infrared images can be readily obscured by complex background clutter. Most existing methods primarily concentrate on local modeling, but they lack sufficient feature interactions, leading to the loss of valuable feature information. Hence, we propose a straightforward and efficient cross-layer feature guided multiscale network (CMNet) for the ISTD. We devise the adjacent layer feature guidance strategy (ALFG) to boost the expressiveness of low-level features through the utilization of semantic guidance from high-level features, enabling a more effective capture of target feature, shape, and structure information. Furthermore, we introduce the multiscale residual connection block (MRCB) that thoroughly leverages multiscale feature information, consequently augmenting perceptual ability and feature representation. For our proposed CMNet, the Intersection over Union (IoU) on the NUAA-SIRST and NUDT-SIRST datasets is 83.32% and 95.74%, while the normalized Intersection over Union (nIoU) is 83.37% and 95.61%, respectively. CMNet exhibits markedly superior detection performance compared to other networks for infrared small target detection. The code is available at github.com/YuanMortal/CMNet.