改进红外小目标检测的损失函数和卷积核，提升稳定性和定位精度。
Revisiting the Scale Loss Function and Gaussian-Shape Convolution for Infrared Small Target Detection
- 用基于差分面积的损失函数，实现梯度单调，训练更稳定。
- 提出高斯形状卷积核，匹配小目标中心密集特征，提升空间感知。
- 适用于红外成像中微弱小目标检测，适合工业与军事应用。
红外小目标检测仍面临两大挑战：非单调尺度损失导致训练不稳定，通用卷积核忽略小目标物理成像特性，造成空间注意力不足。本文重新审视这两个方面：在损失层面，提出一种基于差分面积的尺度损失（diff-based scale loss），根据预测掩码与真实掩码的有向面积差对预测进行加权，获得严格单调的梯度，实现稳定收敛；进一步分析了四种尺度损失变体，揭示其几何特性如何影响检测行为。在空间建模方面，引入具有可学习尺度参数的高斯形状卷积核，以匹配红外小目标中心集中式强度分布；并设计旋转针轮掩码，通过直通估计器自适应对齐卷积核方向。在IRSTD-1k、NUDT-SIRST 和 SIRST-UAVB 数据集上的大量实验表明，该方法在 mIoU、P_d 与 F_a 指标上持续优于当前最优方法。代码与预训练模型已公开。
原文摘要 · Abstract (English)
Infrared small target detection still faces two persistent challenges: training instability from non-monotonic scale loss functions, and inadequate spatial attention due to generic convolution kernels that ignore the physical imaging characteristics of small targets. In this paper, we revisit both aspects. For the loss side, we propose a \emph{diff-based scale loss} that weights predictions according to the signed area difference between the predicted mask and the ground truth, yielding strictly monotonic gradients and stable convergence. We further analyze a family of four scale loss variants to understand how their geometric properties affect detection behavior. For the spatial side, we introduce \emph{Gaussian-shaped convolution} with a learnable scale parameter to match the center-concentrated intensity profile of infrared small targets, and augment it with a \emph{rotated pinwheel mask} that adaptively aligns the kernel with target orientation via a straight-through estimator. Extensive experiments on IRSTD-1k, NUDT-SIRST, and SIRST-UAVB demonstrate consistent improvements in $mIoU$, $P_d$, and $F_a$ over state-of-the-art methods. We release our anonymous code and pretrained models.
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