# Boosting Infrared Small Target Detection via Logit-Domain Contrast and Adaptive Shape Refinement

arXiv: https://arxiv.org/abs/2607.01555 · HTML: https://mlsanity.com/paper/2607.01555/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Asymmetric Contextual Modulation for Infrared Small Target Detection](https://mlsanity.com/paper/2009.14530/) (2020)
- [Dense Nested Attention Network for Infrared Small Target Detection](https://mlsanity.com/paper/2106.00487/) (2021)
- [Attentional Local Contrast Networks for Infrared Small Target Detection](https://mlsanity.com/paper/2012.08573/) (2020)
- [Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection](https://mlsanity.com/paper/1703.09157/) (2017)
- [Infrared Small Target Detection with Scale and Location Sensitivity](https://mlsanity.com/paper/2403.19366/) (2024)
- [Pinwheel-shaped Convolution and Scale-based Dynamic Loss for Infrared Small Target Detection](https://mlsanity.com/paper/2412.16986/) (2024)
- [Deep learning based infrared small object segmentation: Challenges and future directions](https://mlsanity.com/paper/2502.14168/) (2025)
- [DISTA-Net: Dynamic Closely-Spaced Infrared Small Target Unmixing](https://mlsanity.com/paper/2505.19148/) (2025)
- [SAMamba: Adaptive State Space Modeling with Hierarchical Vision for Infrared Small Target Detection](https://mlsanity.com/paper/2505.23214/) (2025)
- [Dynamic High-frequency Convolution for Infrared Small Target Detection](https://mlsanity.com/paper/2602.02969/) (2026)
- [RRCANet: Recurrent Reusable-Convolution Attention Network for Infrared Small Target Detection](https://mlsanity.com/paper/2506.02393/) (2025)
- [Target Driven Adaptive Loss For Infrared Small Target Detection](https://mlsanity.com/paper/2506.01349/) (2025)
- [Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2507.02454/) (2025)

## Abstract (arXiv)

Infrared small target detection (IRSTD) remains challenging due to tiny target size, low signal-to-noise ratio, severe foreground-background imbalance, and blurred boundaries in complex scenes. Existing methods usually rely on post-activation probability-domain supervision for discrimination, where weak targets and strong clutter may produce saturated and close probabilities, limiting weak-target discrimination. Meanwhile, blurred boundaries and halo-like predictions mainly stem from thermal diffusion, tiny target scale, boundary uncertainty, and insufficient explicit contour constraints. To address these issues, we propose Adaptive-Contrastive SLSIoU (AC-SLSIoU), a plug-and-play discriminative and shape-aware loss for IRSTD. Specifically, a Logit-Domain Margin Constraint (LDMC) is introduced to enlarge the response gap between targets and informative hard negatives in the logit space, thereby enhancing weak-target discrimination. Adaptive Boundary Suppression (ABS) applies scale-aware annular penalties to refine target contours and suppress halo-like overflow responses. In addition, False-Alarm Focal Loss assigns larger weights to high-probability negative samples, further penalizing persistent high-confidence false alarms. Without introducing extra inference overhead, the proposed method can be seamlessly integrated into existing detectors and consistently improves both detection accuracy and shape quality. Extensive experiments and cross-backbone evaluations demonstrate the effectiveness, robustness, and generalization ability of the proposed method for infrared small target detection.