提出统一架构SPEGNet，高效检测伪装目标，兼顾精度与速度。
SPEGNet: Synergistic Perception-Guided Network for Camouflaged Object Detection
- 通过通道校准与空间增强融合多尺度特征，统一设计减少冗余组件。
- 在CAMO、COD10K、NC4K数据集上分别达到0.887、0.890、0.895的Sα值。
- 适合处理小目标、复杂纹理和模糊边界的伪装物体检测任务。
伪装目标检测需分割与背景高度相似且边界模糊的物体。现有方法堆叠边界模块、注意力机制和多尺度处理器，导致计算负担重且常降低分辨率，损失关键细节。本文提出SPEGNet，通过统一架构整合多尺度特征，利用通道校准与空间增强实现上下文丰富的表示，使边界自然涌现并保持语义-空间对齐。渐进式精炼采用自适应尺度调制，在中等分辨率下达到最佳边缘影响。该设计平衡了边界精度与区域一致性。SPEGNet在CAMO、COD10K、NC4K数据集上分别取得0.887、0.890、0.895的Sα值，支持实时推理。方法在从微小到大型、从复杂纹理到遮挡场景中均表现优异。代码、模型权重及结果已开源。
原文摘要 · Abstract (English)
Camouflaged object detection segments objects with intrinsic similarity and edge disruption. Current detection methods rely on accumulated complex components. Each approach adds components such as boundary modules, attention mechanisms, and multi-scale processors independently. This accumulation creates a computational burden without proportional gains. To manage this complexity, they process at reduced resolutions, eliminating fine details essential for camouflage. We present SPEGNet, addressing fragmentation through a unified design. The architecture integrates multi-scale features via channel calibration and spatial enhancement. Boundaries emerge directly from context-rich representations, maintaining semantic-spatial alignment. Progressive refinement implements scale-adaptive edge modulation with peak influence at intermediate resolutions. This design strikes a balance between boundary precision and regional consistency. SPEGNet achieves 0.887 $S_α$ on CAMO, 0.890 on COD10K, and 0.895 on NC4K, with real-time inference speed. Our approach excels across scales, from tiny, intricate objects to large, pattern-similar ones, while handling occlusion and ambiguous boundaries. Code, model weights, and results are available on \href{https://github.com/Baber-Jan/SPEGNet}{https://github.com/Baber-Jan/SPEGNet}.
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