Self-Aware Object Detection via Degradation Manifolds

• URL: http://arxiv.org/abs/2602.18394v1
• Date: Fri, 20 Feb 2026 17:58:46 GMT
• Title: Self-Aware Object Detection via Degradation Manifolds
• Authors: Stefan Becker, Simon Weiss, Wolfgang Hübner, Michael Arens,
• Abstract summary: In safety-critical settings, it is insufficient to produce predictions without assessing whether the input remains within the detector's nominal operating regime.<n>We introduce a degradation-aware self-awareness framework based on degradation manifold.<n>Our method augments a standard detection backbone with a lightweight embedding head trained via contrastive learning.
• Score: 3.8265249634979734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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