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[2508.06878] Seeing Through the Noise: Improving Infrared Small Target Detection and Segmentation from Noise Suppression Perspective
Summary
This paper presents a novel approach to infrared small target detection and segmentation (IRSTDS) by introducing a noise-suppression feature pyramid network (NS-FPN) that enhances performance while reducing false alarms.
Why It Matters
Improving IRSTDS is crucial for both defense and civilian applications, where accurate detection in noisy environments can significantly impact operational effectiveness. This research addresses limitations in current CNN-based methods by focusing on noise suppression, potentially leading to advancements in various fields reliant on infrared imaging.
Key Takeaways
- The NS-FPN integrates noise suppression techniques to enhance target detection.
- The LFP module purifies high-frequency components to reduce noise interference.
- The SFS module improves feature fusion through spiral-aware sampling.
- Extensive experiments show significant reductions in false alarms.
- The method is lightweight and easily adaptable to existing frameworks.
Computer Science > Computer Vision and Pattern Recognition arXiv:2508.06878 (cs) [Submitted on 9 Aug 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Seeing Through the Noise: Improving Infrared Small Target Detection and Segmentation from Noise Suppression Perspective Authors:Maoxun Yuan, Duanni Meng, Ziteng Xi, Tianyi Zhao, Shiji Zhao, Yimian Dai, Xingxing Wei View a PDF of the paper titled Seeing Through the Noise: Improving Infrared Small Target Detection and Segmentation from Noise Suppression Perspective, by Maoxun Yuan and 5 other authors View PDF HTML (experimental) Abstract:Infrared small target detection and segmentation (IRSTDS) is a critical yet challenging task in defense and civilian applications, owing to the dim, shapeless appearance of targets and severe background clutter. Recent CNN-based methods have achieved promising target perception results, but they only focus on enhancing feature representation to offset the impact of noise, which results in the increased false alarm problem. In this paper, through analyzing the problem from the frequency domain, we pioneer in improving performance from noise suppression perspective and propose a novel noise-suppression feature pyramid network (NS-FPN), which integrates a low-frequency guided feature purification (LFP) module and a spiral-aware feature sampling (SFS) module into the original FPN structure. The LFP module suppresses the noise features by purifying high-frequency components to achieve f...