Toward Realistic Camouflaged Object Detection: Benchmarks and Method

2025-01-13

AI Summary

Real-world camouflage object detection (RCOD) faces challenges due to high visual similarity between camouflaged objects and backgrounds, resulting in poor localization accuracy and a trade-off between efficiency and precision. Method: We propose the Camouflage-Aware Feature Refinement (CAFR) framework—a novel detector architecture integrating large-model priors into feature learning via Adaptive Gradient Propagation (AGP) and Sparse Feature Refinement (SFR) modules. Leveraging a Transformer backbone, CAFR employs sparse attention and multi-scale refinement for efficient, robust detection. Contribution/Results: We introduce the first RCOD benchmark tailored for detection—constructed by expert re-annotation of three existing camouflage segmentation datasets. On this benchmark, CAFR achieves an 8.2% mAP improvement over state-of-the-art detectors, significantly enhancing localization accuracy and robustness for low-contrast camouflaged objects. Both code and dataset are publicly released.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Feature Construction/Reformulation
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Data transparency and provenance

Abstract

Camouflaged object detection (COD) primarily relies on semantic or instance segmentation methods. While these methods have made significant advancements in identifying the contours of camouflaged objects, they may be inefficient or cost-effective for tasks that only require the specific location of the object. Object detection algorithms offer an optimized solution for Realistic Camouflaged Object Detection (RCOD) in such cases. However, detecting camouflaged objects remains a formidable challenge due to the high degree of similarity between the features of the objects and their backgrounds. Unlike segmentation methods that perform pixel-wise comparisons to differentiate between foreground and background, object detectors omit this analysis, further aggravating the challenge. To solve this problem, we propose a camouflage-aware feature refinement (CAFR) strategy. Since camouflaged objects are not rare categories, CAFR fully utilizes a clear perception of the current object within the prior knowledge of large models to assist detectors in deeply understanding the distinctions between background and foreground. Specifically, in CAFR, we introduce the Adaptive Gradient Propagation (AGP) module that fine-tunes all feature extractor layers in large detection models to fully refine class-specific features from camouflaged contexts. We then design the Sparse Feature Refinement (SFR) module that optimizes the transformer-based feature extractor to focus primarily on capturing class-specific features in camouflaged scenarios. To facilitate the assessment of RCOD tasks, we manually annotate the labels required for detection on three existing segmentation COD datasets, creating a new benchmark for RCOD tasks. Code and datasets are available at: https://github.com/zhimengXin/RCOD.

Problem

• Camouflage Detection
• Efficiency
• Cost-effectiveness

Innovation

• Adaptive Gradient Propagation
• Sparse Feature Refinement
• Camouflage Object Detection