SPEGNet: Synergistic Perception-Guided Network for Camouflaged Object Detection

2025-10-06

AI Summary

Existing camouflaged object detection methods suffer from computational redundancy due to stacked boundary modules and attention mechanisms, and often operate at low resolutions, leading to loss of critical edge details, edge discontinuities, and fragmentation. To address these issues, we propose a Collaborative Perception-Guided Unified Network (CPG-UNet). It jointly models contextual information via channel calibration and spatial enhancement; introduces multi-scale feature fusion and progressive boundary optimization to achieve scale-adaptive edge modulation at intermediate resolution—balancing semantic consistency and fine-grained detail preservation. Extensive experiments demonstrate state-of-the-art performance: Sα scores of 0.887, 0.890, and 0.895 on CAMO, COD10K, and NC4K benchmarks, respectively. Our method significantly improves detection accuracy for small objects, large background-similar objects, and heavily occluded scenarios, while maintaining real-time inference capability (≈32 FPS on a single RTX 3090).

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Learning on the Edge & Model Compression
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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}.

Problem

• Detecting camouflaged objects with intrinsic similarity and edge disruption
• Reducing computational burden from accumulated complex detection components
• Maintaining boundary precision and regional consistency across object scales

Innovation

• Unified network integrates multi-scale features via calibration
• Boundaries emerge directly from context-rich spatial representations
• Progressive refinement implements scale-adaptive edge modulation