# CourtNet for Infrared Small-Target Detection

arXiv: https://arxiv.org/abs/2209.13780 · HTML: https://mlsanity.com/paper/2209.13780/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 60 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## 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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [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)
- [Model Complexity of Deep Learning: A Survey](https://mlsanity.com/paper/2103.05127/) (2021)
- [Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm](https://mlsanity.com/paper/1810.03173/) (2018)
- [DRPN: Making CNN Dynamically Handle Scale Variation](https://mlsanity.com/paper/2112.10963/) (2021)

## Built on by (most cited first, 1 of 1)

- [Dynamic Background Reconstruction via MAE for Infrared Small Target Detection](https://mlsanity.com/paper/2301.04497/) (2023)

## Abstract (arXiv)

Infrared small-target detection (ISTD) is an important computer vision task. ISTD aims at separating small targets from complex background clutter. The infrared radiation decays over distances, making the targets highly dim and prone to confusion with the background clutter, which makes the detector challenging to balance the precision and recall rate. To deal with this difficulty, this paper proposes a neural-network-based ISTD method called CourtNet, which has three sub-networks: the prosecution network is designed for improving the recall rate; the defendant network is devoted to increasing the precision rate; the jury network weights their results to adaptively balance the precision and recall rate. Furthermore, the prosecution network utilizes a densely connected transformer structure, which can prevent small targets from disappearing in the network forward propagation. In addition, a fine-grained attention module is adopted to accurately locate the small targets. Experimental results show that CourtNet achieves the best F1-score on the two ISTD datasets, MFIRST (0.62) and SIRST (0.73).