[2605.00886] Selective Attention-Based Network for Robust Infrared Small Target Detection
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.00886 (cs)
[Submitted on 27 Apr 2026]
Title:Selective Attention-Based Network for Robust Infrared Small Target Detection
Authors:Yingming Zhang, Wuqi Su, Qing Xiao, Yonggang Yang
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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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Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.00886 [cs.CV]
(or arXiv:2605.00886v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.00886
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arXiv-issued DOI via DataCite
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From: Wuqi Su [view email] [v1] Mon, 27 Apr 2026 09:17:23 UTC (21 KB)
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