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

TL;DR

CCDNet is a novel infrared small target detection network that effectively suppresses background distractions and distractors, improving detection accuracy in complex scenes.

Contribution

The paper introduces CCDNet, combining a multi-branch backbone, fusion neck, and contrastive distractor discriminator for enhanced camouflage-aware infrared detection.

Findings

CCDNet outperforms state-of-the-art methods on infrared datasets.

The proposed CaDD reduces false alarms by discriminating distractors.

The network effectively highlights targets in complex backgrounds.

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.