CCDNet: Learning to Detect Camouflage against Distractors in Infrared Small Target Detection

2026-03-30

AI Summary

This work addresses the challenge of detecting infrared small targets, which often exhibit low contrast and blend into complex backgrounds, leading to high false alarm rates due to interference from clutter with similar characteristics. To overcome this, the authors propose CCDNet, a novel framework that jointly models camouflage awareness and interference suppression. Specifically, a weighted multi-branch perceptron extracts self-conditioned multi-level features, while an Aggregation-Refinement Fusion Neck (ARFN) bidirectionally reconstructs the relationship between target and background. Additionally, a Contrastive auxiliary Disturbance Discriminator (CaDD) adaptively distinguishes genuine targets from distractors at both local and global scales. Extensive experiments demonstrate that CCDNet significantly outperforms state-of-the-art methods across multiple infrared datasets, achieving notable advances in both detection accuracy and robustness against interference.

Abstract

Infrared target detection (IRSTD) tasks have critical applications in areas like wilderness rescue and maritime search. However, detecting infrared targets is challenging due to their low contrast and tendency to blend into complex backgrounds, effectively camouflaging themselves. Additionally, other objects with similar features (distractors) can cause false alarms, further degrading detection performance. To address these issues, we propose a novel \textbf{C}amouflage-aware \textbf{C}ounter-\textbf{D}istraction \textbf{Net}work (CCDNet) in this paper. We design a backbone with Weighted Multi-branch Perceptrons (WMPs), which aggregates self-conditioned multi-level features to accurately represent the target and background. Based on these rich features, we then propose a novel Aggregation-and-Refinement Fusion Neck (ARFN) to refine structures/semantics from shallow/deep features maps, and bidirectionally reconstruct the relations between the targets and the backgrounds, highlighting the targets while suppressing the complex backgrounds to improve detection accuracy. Furthermore, we present a new Contrastive-aided Distractor Discriminator (CaDD), enforcing adaptive similarity computation both locally and globally between the real targets and the backgrounds to more precisely discriminate distractors, so as to reduce the false alarm rate. Extensive experiments on infrared image datasets confirm that CCDNet outperforms other state-of-the-art methods.

Problem

• infrared small target detection
• camouflage
• distractors
• false alarms
• low contrast

Innovation

• Camouflage-aware
• Counter-Distraction
• Weighted Multi-branch Perceptrons
• Aggregation-and-Refinement Fusion Neck
• Contrastive-aided Distractor Discriminator