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This paper introduces CCDNet, a novel neural network architecture for infrared small target detection designed to improve performance in camouflaged environments with distractors. CCDNet employs Weighted Multi-branch Perceptrons (WMPs) in its backbone to aggregate multi-level features, an Aggregation-and-Refinement Fusion Neck (ARFN) to refine feature maps and model target-background relations, and a Contrastive-aided Distractor Discriminator (CaDD) to reduce false alarms. Experiments on infrared datasets demonstrate that CCDNet achieves state-of-the-art performance by effectively highlighting targets and suppressing complex backgrounds.
By explicitly modeling camouflage and distractors, CCDNet achieves state-of-the-art infrared small target detection, even in challenging environments where targets blend into the background.
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.
