Breathing New Life into Small Object Detection with Detection-Oriented Rectification.

Abstract

Small Object Detection (SOD) is fundamentally constrained by the inherent scarcity of visual cues in size-limited instances. This low-entropy nature frequently induces ambiguity and collapse in the learned feature space, critically undermining the efficacy of downstream tasks. Restoration-based methods offer a promising, albeit flawed, solution to this representational bottleneck. On one hand, they excel at recovering fine-grained details; on the other, their effectiveness is compromised by a reliance on synthetic corruptions that generalize poorly at inference, a problem compounded by the inherent conflict between pixel-level fidelity and semantic abstraction. To overcome these limitations, we introduce Detection-Oriented RectificAtion (DORA), a unified framework built upon a novel degradation-then-rectification paradigm. The central insight lies in the principle: knowing what degrades, knowing how to rectify. DORA first explicitly learns to deconstruct complex visual corruptions into a versatile, learnable degradation basis set, providing a structured understanding of the inherent degradation of small instances. This encoded knowledge then forms the dynamic degradation-conditioned prompt, initiating a task-oriented rectification and effectively mitigating the distribution shift at inference. Furthermore, on the foundation of a preceding entity reconstruction task, we devise a synergistic contrastive function to alleviate the task conflict by cyclically aligning rectified entity embeddings with detection-friendly exemplars, thereby robustly bridging the granularity gap between detection and rectification, ultimately facilitating a harmonious optimization of the entire framework. As a paradigm-agnostic solution, DORA can be seamlessly integrated with a wide range of detectors. Comprehensive experiments on five challenging SOD datasets showcase the consistent and substantial performance gains across diverse architectures, underscoring the efficacy and broad potential of our task-oriented rectification strategy.