DCCS-Det: Directional Context and Cross-Scale-Aware Detector for Infrared Small Target
Shuying Li, Qiang Ma, San Zhang, and Chuang Yang

TL;DR
DCCS-Det introduces a novel infrared small target detector that combines directional context and cross-scale feature extraction to improve detection accuracy in complex backgrounds.
Contribution
The paper presents DCCS-Det, a new detector with dual modules for enhanced local-global feature modeling and noise suppression, outperforming existing methods.
Findings
Achieves state-of-the-art detection accuracy on multiple datasets.
Effective in complex backgrounds with low-contrast targets.
Validated through extensive ablation studies.
Abstract
Infrared small target detection (IRSTD) is critical for applications like remote sensing and surveillance, which aims to identify small, low-contrast targets against complex backgrounds. However, existing methods often struggle with inadequate joint modeling of local-global features (harming target-background discrimination) or feature redundancy and semantic dilution (degrading target representation quality). To tackle these issues, we propose DCCS-Det (Directional Context and Cross-Scale Aware Detector for Infrared Small Target), a novel detector that incorporates a Dual-stream Saliency Enhancement (DSE) block and a Latent-aware Semantic Extraction and Aggregation (LaSEA) module. The DSE block integrates localized perception with direction-aware context aggregation to help capture long-range spatial dependencies and local details. On this basis, the LaSEA module mitigates feature…
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Taxonomy
TopicsInfrared Target Detection Methodologies · Advanced Neural Network Applications · Visual Attention and Saliency Detection
