# Towards Robust Infrared Small Target Detection: A Feature-Enhanced and Sensitivity-Tunable Framework

arXiv: https://arxiv.org/abs/2407.20090 · HTML: https://mlsanity.com/paper/2407.20090/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (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)
- [UIU-Net: U-Net in U-Net for Infrared Small Object Detection](https://mlsanity.com/paper/2212.00968/) (2022)
- [Attentional Local Contrast Networks for Infrared Small Target Detection](https://mlsanity.com/paper/2012.08573/) (2020)
- [MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection](https://mlsanity.com/paper/2209.13756/) (2022)
- [Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines](https://mlsanity.com/paper/2406.14482/) (2024)
- [One-Stage Cascade Refinement Networks for Infrared Small Target Detection](https://mlsanity.com/paper/2212.08472/) (2022)
- [Multi-Scale Direction-Aware Network for Infrared Small Target Detection](https://mlsanity.com/paper/2406.02037/) (2024)
- [Rediscovering BCE Loss for Uniform Classification](https://mlsanity.com/paper/2403.07289/) (2024)
- [Detection of Clouds in Multiple Wind Velocity Fields using Ground-based Infrared Sky Images](https://mlsanity.com/paper/2105.03535/) (2021)
- [In Defense and Revival of Bayesian Filtering for Thermal Infrared Object Tracking](https://mlsanity.com/paper/2402.17098/) (2024)

## Built on by (most cited first, 8 of 8)

- [From Easy to Hard: Progressive Active Learning Framework for Infrared Small Target Detection with Single Point Supervision](https://mlsanity.com/paper/2412.11154/) (2024)
- [Refined Infrared Small Target Detection Scheme with Single-Point Supervision](https://mlsanity.com/paper/2408.02773/) (2024)
- [Rethinking Generalizable Infrared Small Target Detection: A Real-scene Benchmark and Cross-view Representation Learning](https://mlsanity.com/paper/2504.16487/) (2025)
- [LR-Net: A Lightweight and Robust Network for Infrared Small Target Detection](https://mlsanity.com/paper/2408.02780/) (2024)
- [The First Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results](https://mlsanity.com/paper/2408.09615/) (2024)
- [HyperTea: A Hypergraph-based Temporal Enhancement and Alignment Network for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2508.10678/) (2025)
- [Rethinking Infrared Small Target Detection: A Foundation-Driven Efficient Paradigm](https://mlsanity.com/paper/2512.05511/) (2025)
- [RDANet: Relative Degradation Aware Network for Infrared Small Target Detection](https://mlsanity.com/paper/2608.20870/) (2026)

## Abstract (arXiv)

Recently, single-frame infrared small target (SIRST) detection technology has attracted widespread attention. Different from most existing deep learning-based methods that focus on improving network architectures, we propose a feature-enhanced and sensitivity-tunable (FEST) framework, which is compatible with existing SIRST detection networks and further enhances their detection performance. The FEST framework improves the model's robustness from two aspects: feature enhancement and target confidence regulation. For feature enhancement, we employ a multi-scale fusion strategy to improve the model's perception to multi-scale features of multi-size targets, and design an edge enhancement difficulty mining (EEDM) loss to guide the network to continuously focus on challenging target regions and edge features during training. For target confidence regulation, an adjustable sensitivity (AS) strategy is proposed for network post-processing. This strategy enhances the model's adaptability in complex scenarios and significantly improves the detection rate of infrared small targets while maintaining segmentation accuracy. Extensive experimental results show that our FEST framework can effectively enhance the performance of existing SIRST detection networks. The code is available at https://github.com/YuChuang1205/FEST-Framework