Infrared small target detection faces the inherent challenge of precisely localizing dim targets amidst complex background clutter. Traditional approaches struggle to balance detection precision and false alarm rates. To break this dilemma, we propose SeRankDet, a deep network that achieves high accuracy beyond the conventional hit-miss trade-off, by following the “Pick of the Bunch” principle. At its core lies our selective rank-aware attention (SeRank) module, employing a nonlinear Top-K selection process that preserves the most salient responses, preventing target signal dilution while maintaining constant complexity. Furthermore, we replace the static concatenation typical in U-Net structures with our large selective feature fusion (LSFF) module, a dynamic fusion strategy that empowers SeRankDet with adaptive feature integration, enhancing its ability to discriminate true targets from false alarms. The network’s discernment is further refined by our dilated difference convolution (DDC) module, which merges differential convolution aimed at amplifying subtle target characteristics with dilated convolution to expand the receptive field, thereby substantially improving target-background separation. Despite its lightweight architecture, the proposed SeRankDet sets new benchmarks in state-of-the-art performance across multiple public datasets. The code is available at https://github.com/GrokCV/SeRankDet .

中文翻译：

---

精选：通过选择性排名感知注意力来检测红外小目标，超越命中与错过的权衡


红外小目标检测面临着在复杂背景杂波中精确定位昏暗目标的固有挑战。传统方法很难平衡检测精度和误报率。为了打破这一困境，我们提出了 SeRankDet，这是一种深度网络，通过遵循“Pick of the Bunch”原则，可以实现超越传统的命中-未命中权衡的高精度。其核心在于我们的选择性排名感知注意力 (SeRank) 模块，采用非线性 Top-K 选择过程，保留最显着的响应，防止目标信号稀释，同时保持恒定的复杂性。此外，我们用我们的大型选择性特征融合（LSFF）模块取代了 U-Net 结构中典型的静态串联，这是一种动态融合策略，使 SeRankDet 具有自适应特征集成，增强了其区分真实目标和错误警报的能力。我们的扩张差分卷积（DDC）模块进一步细化了网络的识别能力，该模块将旨在放大细微目标特征的差分卷积与扩大感受野的扩张卷积相结合，从而显着改善目标与背景的分离。尽管其架构是轻量级的，但所提出的 SeRankDet 为多个公共数据集的最先进性能树立了新的基准。代码可在 https://github.com/GrokCV/SeRankDet 获取。