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<p>Detecting small infrared targets is a critical challenge in computer vision, focusing on identifying and localizing minimal-pixel-sized targets within infrared imagery. This task faces significant challenges due to the extremely small size of the targets, variability in target dimensions and shapes across diverse scenes, intricate background complexities, and instances of target occlusion. In this study, we introduce an enhanced U-Net model featuring three novel modules: the Residual Coordinate Attention Block (RCA-Block) integrates coordinate attention and residual structures to enhance feature representation; the Dynamic Context-aware Multi-scale Fusion Module (DCMFM) dynamically fuses multi-scale features based on target characteristics; and the Multi-dilation Depthwise Separable Convolutional Module (MDSCM) employs multi-dilation depthwise separable convolutions to capture spatial features across varying receptive fields. Experiments demonstrate that the proposed model synergizes multi-scale feature fusion and spatial information enhancement, achieving superior accuracy in small target detection, robust noise resistance.</p>

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Infrared small target detection using multi-scale attention and dilated separable convolution

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Wenjuan Tang,
  • Qun Dai,
  • Yuning Zhu,
  • Rui Ma

摘要

Detecting small infrared targets is a critical challenge in computer vision, focusing on identifying and localizing minimal-pixel-sized targets within infrared imagery. This task faces significant challenges due to the extremely small size of the targets, variability in target dimensions and shapes across diverse scenes, intricate background complexities, and instances of target occlusion. In this study, we introduce an enhanced U-Net model featuring three novel modules: the Residual Coordinate Attention Block (RCA-Block) integrates coordinate attention and residual structures to enhance feature representation; the Dynamic Context-aware Multi-scale Fusion Module (DCMFM) dynamically fuses multi-scale features based on target characteristics; and the Multi-dilation Depthwise Separable Convolutional Module (MDSCM) employs multi-dilation depthwise separable convolutions to capture spatial features across varying receptive fields. Experiments demonstrate that the proposed model synergizes multi-scale feature fusion and spatial information enhancement, achieving superior accuracy in small target detection, robust noise resistance.