Infrared small target detection (IRSTD) is critical for applications like remote sensing and surveillance, which aims to identify small, low-contrast targets against complex backgrounds. However, existing methods often struggle with inadequate joint modeling of local-global features (harming target-background discrimination) or feature redundancy and semantic dilution (degrading target representation quality). To tackle these issues, we propose DCCS-Det (Directional Context and Cross-Scale Aware Detector for Infrared Small Target), a novel detector that incorporates a Dual-stream Saliency Enhancement (DSE) block and a Latent-aware Semantic Extraction and Aggregation (LaSEA) module. The DSE block integrates localized perception with direction-aware context aggregation to help capture long-range spatial dependencies and local details. On this basis, the LaSEA module mitigates feature degradation via cross-scale feature extraction and random pooling sampling strategies, enhancing discriminative features and suppressing noise. Extensive experiments show that DCCS-Det achieves state-of-the-art detection accuracy with competitive efficiency across multiple datasets. Ablation studies further validate the contributions of DSE and LaSEA in improving target perception and feature representation under complex scenarios. \href{https://huggingface.co/InPeerReview/InfraredSmallTargetDetection-IRSTD.DCCS}{DCCS-Det Official Code is Available Here!}

翻译：红外小目标检测（IRSTD）对于遥感与监视等应用至关重要，其目标是在复杂背景中识别出尺寸小、对比度低的目标。然而，现有方法通常面临局部-全局特征联合建模不足（损害目标-背景区分能力）或特征冗余与语义稀释（降低目标表征质量）的问题。为解决这些问题，我们提出了DCCS-Det（用于红外小目标检测的方向性上下文与跨尺度感知检测器），这是一种新型检测器，它融合了双流显著性增强（DSE）模块和潜在感知语义提取与聚合（LaSEA）模块。DSE模块将局部感知与方向感知的上下文聚合相结合，以帮助捕获长程空间依赖性和局部细节。在此基础上，LaSEA模块通过跨尺度特征提取和随机池化采样策略来缓解特征退化，从而增强判别性特征并抑制噪声。大量实验表明，DCCS-Det在多个数据集上以具有竞争力的效率实现了最先进的检测精度。消融研究进一步验证了DSE和LaSEA模块在复杂场景下对提升目标感知和特征表征能力的贡献。\href{https://huggingface.co/InPeerReview/InfraredSmallTargetDetection-IRSTD.DCCS}{DCCS-Det官方代码已在此发布！}