Object detectors achieve strong performance under nominal imaging conditions but can fail silently when exposed to blur, noise, compression, adverse weather, or resolution changes. In safety-critical settings, it is therefore insufficient to produce predictions without assessing whether the input remains within the detector's nominal operating regime. We refer to this capability as self-aware object detection. We introduce a degradation-aware self-awareness framework based on degradation manifolds, which explicitly structure a detector's feature space according to image degradation rather than semantic content. Our method augments a standard detection backbone with a lightweight embedding head trained via multi-layer contrastive learning. Images sharing the same degradation composition are pulled together, while differing degradation configurations are pushed apart, yielding a geometrically organized representation that captures degradation type and severity without requiring degradation labels or explicit density modeling. To anchor the learned geometry, we estimate a pristine prototype from clean training embeddings, defining a nominal operating point in representation space. Self-awareness emerges as geometric deviation from this reference, providing an intrinsic, image-level signal of degradation-induced shift that is independent of detection confidence. Extensive experiments on synthetic corruption benchmarks, cross-dataset zero-shot transfer, and natural weather-induced distribution shifts demonstrate strong pristine-degraded separability, consistent behavior across multiple detector architectures, and robust generalization under semantic shift. These results suggest that degradation-aware representation geometry provides a practical and detector-agnostic foundation.

翻译：目标检测器在标准成像条件下表现出色，但在面对模糊、噪声、压缩、恶劣天气或分辨率变化时可能悄然失效。在安全关键场景中，仅生成预测而不评估输入是否仍处于检测器的标称工作状态是不够的。我们将这种能力称为自感知目标检测。我们提出一种基于退化流形的退化感知自感知框架，该框架根据图像退化而非语义内容来显式构建检测器的特征空间结构。我们的方法通过多层对比学习训练一个轻量级嵌入头来增强标准检测骨干网络。具有相同退化构成的图像被拉近，而不同退化配置的图像被推远，从而产生一种几何结构化的表示，能够捕捉退化类型和严重程度，且无需退化标签或显式密度建模。为了锚定所学几何结构，我们从干净训练嵌入中估计一个原始原型，在表示空间中定义一个标称工作点。自感知表现为与该参考点的几何偏离，提供了一个独立于检测置信度的、图像级别的退化诱导偏移内在信号。在合成损坏基准测试、跨数据集零样本迁移以及自然天气引起的分布偏移上的大量实验表明，该方法具有强大的原始-退化可分离性，在多种检测器架构上表现一致，并在语义偏移下展现出稳健的泛化能力。这些结果表明，退化感知的表示几何为自感知目标检测提供了一个实用且与检测器无关的基础。