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 non-linear 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.

翻译：红外小目标检测面临在复杂背景杂波中精确定位微弱目标的固有挑战。传统方法难以平衡检测精度与虚警率。为突破此困境，我们提出SeRankDet深度网络，其遵循“择优而检”原则，实现了超越传统命中-漏检权衡的高精度检测。其核心是我们提出的选择性秩感知注意力（SeRank）模块，该模块采用非线性Top-K选择过程，保留最显著的响应，在维持恒定计算复杂度的同时防止目标信号稀释。此外，我们以大型选择性特征融合（LSFF）模块取代U-Net结构中典型的静态拼接操作，该动态融合策略赋予SeRankDet自适应特征整合能力，从而增强其区分真实目标与虚警的效能。网络的判别能力通过我们提出的扩张差分卷积（DDC）模块进一步优化，该模块融合了旨在增强细微目标特征的差分卷积与扩展感受野的扩张卷积，从而显著提升目标-背景分离性能。尽管采用轻量级架构，所提出的SeRankDet在多个公开数据集上仍创造了最先进性能的新基准。代码发布于https://github.com/GrokCV/SeRankDet。