# EFLNet: Enhancing Feature Learning for Infrared Small Target Detection

arXiv: https://arxiv.org/abs/2307.14723 · HTML: https://mlsanity.com/paper/2307.14723/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 27 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Dynamic Head: Unifying Object Detection Heads with Attentions](https://mlsanity.com/paper/2106.08322/) (2021)
- [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)
- [A Normalized Gaussian Wasserstein Distance for Tiny Object Detection](https://mlsanity.com/paper/2110.13389/) (2021)
- [Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision](https://mlsanity.com/paper/2304.01484/) (2023)
- [One-Stage Cascade Refinement Networks for Infrared Small Target Detection](https://mlsanity.com/paper/2212.08472/) (2022)
- [AGPCNet: Attention-Guided Pyramid Context Networks for Infrared Small Target Detection](https://mlsanity.com/paper/2111.03580/) (2021)

## Built on by (most cited first, 15 of 15)

- [Triple-domain Feature Learning with Frequency-aware Memory Enhancement for Moving Infrared Small Target Detection](https://mlsanity.com/paper/2406.06949/) (2024)
- [Seeing Through the Noise: Improving Infrared Small Target Detection and Segmentation from Noise Suppression Perspective](https://mlsanity.com/paper/2508.06878/) (2025)
- [Deep learning based infrared small object segmentation: Challenges and future directions](https://mlsanity.com/paper/2502.14168/) (2025)
- [Text-IRSTD: Leveraging Semantic Text to Promote Infrared Small Target Detection in Complex Scenes](https://mlsanity.com/paper/2503.07249/) (2025)
- [Hybrid Mask Generation for Infrared Small Target Detection with Single-Point Supervision](https://mlsanity.com/paper/2409.04011/) (2024)
- [AuxDet: Auxiliary Metadata Matters for Omni-Domain Infrared Small Target Detection](https://mlsanity.com/paper/2505.15184/) (2025)
- [TY-RIST: Tactical YOLO Tricks for Real-time Infrared Small Target Detection](https://mlsanity.com/paper/2509.22909/) (2025)
- [Single-Point Supervised High-Resolution Dynamic Network for Infrared Small Target Detection](https://mlsanity.com/paper/2408.01976/) (2024)
- [Point-to-Mask: From Arbitrary Point Annotations to Mask-Level Infrared Small Target Detection](https://mlsanity.com/paper/2603.16257/) (2026)
- [Rethinking IRSTD: Single-Point Supervision Guided Encoder-only Framework is Enough for Infrared Small Target Detection](https://mlsanity.com/paper/2604.05363/) (2026)
- [Robust infrared small target detection using self-supervised and a contrario paradigms](https://mlsanity.com/paper/2410.07437/) (2024)
- [ISTD-YOLO: A Multi-Scale Lightweight High-Performance Infrared Small Target Detection Algorithm](https://mlsanity.com/paper/2504.14289/) (2025)
- [Anomaly-Aware YOLO: A Frugal yet Robust Approach to Infrared Small Target Detection](https://mlsanity.com/paper/2510.04741/) (2025)
- [Student Classroom Behavior Recognition Based on Improved YOLOv8s](https://mlsanity.com/paper/2604.27293/) (2026)
- [Denoising-Enhanced Coarse-to-Fine Infrared Small Target Detection with Attention Prior-Guided Knowledge Distillation](https://mlsanity.com/paper/2606.21956/) (2026)

## Abstract (arXiv)

Single-frame infrared small target detection is considered to be a challenging task, due to the extreme imbalance between target and background, bounding box regression is extremely sensitive to infrared small target, and target information is easy to lose in the high-level semantic layer. In this article, we propose an enhancing feature learning network (EFLNet) to address these problems. First, we notice that there is an extremely imbalance between the target and the background in the infrared image, which makes the model pay more attention to the background features rather than target features. To address this problem, we propose a new adaptive threshold focal loss (ATFL) function that decouples the target and the background, and utilizes the adaptive mechanism to adjust the loss weight to force the model to allocate more attention to target features. Second, we introduce the normalized Gaussian Wasserstein distance (NWD) to alleviate the difficulty of convergence caused by the extreme sensitivity of the bounding box regression to infrared small target. Finally, we incorporate a dynamic head mechanism into the network to enable adaptive learning of the relative importance of each semantic layer. Experimental results demonstrate our method can achieve better performance in the detection performance of infrared small target compared to the state-of-the-art (SOTA) deep-learning-based methods. The source codes and bounding box annotated datasets are available at https://github.com/YangBo0411/infrared-small-target.