Selective Attention-Based Network for Robust Infrared Small Target Detection

• URL: http://arxiv.org/abs/2605.00886v1
• Date: Mon, 27 Apr 2026 09:17:23 GMT
• Title: Selective Attention-Based Network for Robust Infrared Small Target Detection
• Abstract summary: Infrared small target detection plays a pivotal role in a broad spectrum of mission-critical applications, including maritime surveillance, military search and rescue, early warning systems, and precision-guided strikes.<n>Despite significant progress driven by deep learning methods, fundamental challenges persist: infrared small targets occupy extremely limited spatial extents, exhibit low signal-to-clutter ratios, and are easily confused with structurally complex backgrounds that frequently induce false alarms.<n>Existing encoder-decoder architectures suffer from two key limitations - an information bottleneck in early convolutional stages, and static skip connections that lack the dynamic adaptability required to discriminate between genuine targets and pseudo-target regions
• Score: 6.998442855153326
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Infrared small target detection (IRSTD) plays a pivotal role in a broad spectrum of mission-critical applications, including maritime surveillance, military search and rescue, early warning systems, and precision-guided strikes, all of which demand the precise identification of dim, sub-pixel targets amid highly cluttered infrared backgrounds. Despite significant progress driven by deep learning methods, fundamental challenges persist: infrared small targets occupy extremely limited spatial extents (often only a few pixels), exhibit low signal-to-clutter ratios, and are easily confused with structurally complex backgrounds that frequently induce false alarms. Existing encoder-decoder architectures suffer from two key limitations - an information bottleneck in early convolutional stages that undermines fine-grained target perception, and static skip connections that lack the dynamic adaptability required to discriminate between genuine targets and pseudo-target regions. To address these challenges, we propose SANet, a Selective Attention-based Network built upon the classical U-Net framework and augmented with two novel components: (1) a \emph{Dual-path Semantic-aware Module} (DSM) that integrates standard convolutions for local spatial detail preservation with pinwheel-shaped convolutions for expanded, direction-sensitive receptive fields, followed by a Convolutional Block Attention Module (CBAM) for fine-grained spatial-channel feature recalibration; and (2) a \emph{Selective Attention Fusion Module} (SAFM) that replaces conventional static skip connections with a spatially adaptive, learnable weighting mechanism to perform context-aware, cross-scale feature fusion.

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