# Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds

arXiv: https://arxiv.org/abs/2109.14379 · HTML: https://mlsanity.com/paper/2109.14379/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 60 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://mlsanity.com/paper/2103.10697/) (2021)
- [Learning Data Augmentation Strategies for Object Detection](https://mlsanity.com/paper/1906.11172/) (2019)
- [Asymmetric Contextual Modulation for Infrared Small Target Detection](https://mlsanity.com/paper/2009.14530/) (2020)
- [An Analysis of Scale Invariance in Object Detection - SNIP](https://mlsanity.com/paper/1711.08189/) (2017)
- [Attentional Local Contrast Networks for Infrared Small Target Detection](https://mlsanity.com/paper/2012.08573/) (2020)
- [PolyTransform: Deep Polygon Transformer for Instance Segmentation](https://mlsanity.com/paper/1912.02801/) (2019)
- [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)
- [Dual-Level Collaborative Transformer for Image Captioning](https://mlsanity.com/paper/2101.06462/) (2021)
- [Contrastive Triple Extraction with Generative Transformer](https://mlsanity.com/paper/2009.06207/) (2020)
- [TBC-Net: A real-time detector for infrared small target detection using semantic constraint](https://mlsanity.com/paper/2001.05852/) (2019)
- [Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm](https://mlsanity.com/paper/1810.03173/) (2018)

## Built on by (most cited first, 12 of 12)

- [SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection](https://mlsanity.com/paper/2401.15583/) (2024)
- [MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection](https://mlsanity.com/paper/2403.02148/) (2024)
- [MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection](https://mlsanity.com/paper/2209.13756/) (2022)
- [TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection](https://mlsanity.com/paper/2402.02046/) (2024)
- [A Multi-task Framework for Infrared Small Target Detection and Segmentation](https://mlsanity.com/paper/2206.06923/) (2022)
- [MPANet: Multi-Patch Attention For Infrared Small Target object Detection](https://mlsanity.com/paper/2206.02120/) (2022)
- [Deep-NFA: a Deep textita contrario Framework for Small Object Detection](https://mlsanity.com/paper/2303.01363/) (2023)
- [Small and Dim Target Detection in IR Imagery: A Review](https://mlsanity.com/paper/2311.16346/) (2023)
- [PixelGame: Infrared small target segmentation as a Nash equilibrium](https://mlsanity.com/paper/2205.13124/) (2022)
- [Local Contrast and Global Contextual Information Make Infrared Small Object Salient Again](https://mlsanity.com/paper/2301.12093/) (2023)
- [D'etection de petites cibles par apprentissage profond et crit`ere a contrario](https://mlsanity.com/paper/2210.00755/) (2022)
- [SFA-UNet: More Attention to Multi-Scale Contrast and Contextual Information in Infrared Small Object Segmentation](https://mlsanity.com/paper/2410.22881/) (2024)

## Abstract (arXiv)

The infrared small-dim target detection is one of the key techniques in the infrared search and tracking system. Since the local regions similar to infrared small-dim targets spread over the whole background, exploring the interaction information amongst image features in large-range dependencies to mine the difference between the target and background is crucial for robust detection. However, existing deep learning-based methods are limited by the locality of convolutional neural networks, which impairs the ability to capture large-range dependencies. Additionally, the small-dim appearance of the infrared target makes the detection model highly possible to miss detection. To this end, we propose a robust and general infrared small-dim target detection method with the transformer. We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range. Moreover, we design a feature enhancement module to learn discriminative features of small-dim targets to avoid miss detection. After that, to avoid the loss of the target information, we adopt a decoder with the U-Net-like skip connection operation to contain more information of small-dim targets. Finally, we get the detection result by a segmentation head. Extensive experiments on two public datasets show the obvious superiority of the proposed method over state-of-the-art methods and the proposed method has stronger cross-scene generalization and anti-noise performance.