README.md

May 10, 2026 · View on GitHub

Detection-Friendly Nonuniformity Correction: A Union Framework for Infrared UAV Target Detection

Authors: Houzhang Fang1, Xiaolin Wang1, Zengyang Li1, Lu Wang1, Qingshan Li1, Yi Chang2, Luxin Yan2

1Xidian University, 2Huazhong University of Science and Technology

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2025

[Paper PDF|IEEE] [Paper PDF|arXiv] [Supplementary Material] [BibTex]

⭐News: Our work has been selected as a highlight at the CVPR 2025 conference!

⭐News: Our new work has been accepted by AAAI 2026：[Blur-Robust Detection via Feature Restoration: An End-to-End Framework for Prior-Guided Infrared UAV Target Detection]

Abstract

Infrared unmanned aerial vehicle (UAV) images captured using thermal detectors are often affected by temperature-dependent low-frequency nonuniformity, which significantly reduces the contrast of the images. Detecting UAV targets under nonuniform conditions is crucial in UAV surveillance applications. Existing methods typically treat infrared nonuniformity correction (NUC) as a preprocessing step for detection, which leads to suboptimal performance. Balancing the two tasks while enhancing detection-beneficial information remains challenging. In this paper, we present a detection-friendly union framework, termed UniCD, that simultaneously addresses both infrared NUC and UAV target detection tasks in an end-to-end manner. We first model NUC as a small number of parameter estimation problem jointly driven by priors and data to generate detection-conducive images. Then, we incorporate a new auxiliary loss with target mask supervision into the backbone of the infrared UAV target detection network to strengthen target features while suppressing the background. To better balance correction and detection, we introduce a detection-guided self-supervised loss to reduce feature discrepancies between the two tasks, thereby enhancing detection robustness to varying nonuniformity levels. Additionally, we construct a new benchmark composed of 50,000 infrared images in various nonuniformity types, multi-scale UAV targets and rich backgrounds with target annotations, called IRBFD. Extensive experiments on IRBFD demonstrate that our UniCD is a robust union framework for NUC and UAV target detection while achieving real-time processing capabilities.

Network Architecture

accessibility text

Overall architecture of the proposed UniCD.

Our custom-built dataset (IRBFD) can be downloaded via the following link:

If you find the IRBFD dataset useful for your research, please consider citing our paper [BibTex]. Your acknowledgement is greatly appreciated!

Nonuniformity Correction (NUC) Software

We have developed correction software that allows users to upload various non-uniform images for real-time processing. Our method does not require retraining and demonstrates excellent universal applicability across diverse scenarios. Our correction software currently only provides processing results based on the non-uniformity correction algorithm from the CVPR paper and can run on both Linux and Windows systems.

Usage steps:

  • Step 1. Click the “Select Image Files” button to load images (multiple images can be loaded). You can also select an entire image folder for batch processing (by checking the box).
  • Step 2. Choose the output folder where the processed results will be saved. If unchecked, the results will not be saved.
  • Step 3. Click the “Execute Process” button. The software will show the corrected results, and save both the corrected images and the estimated bias field images (by checking the box).

Download Software Here -Baidu Netdisk (passcode: b7y7), Google Drive.

Key Features:

  • No Retraining Required: Immediately applicable to a wide range of infrared nonuniformity images.
  • Universal Applicability: Outstanding performance across different scenes.
  • Real-time Processing: Supports real-time online demonstration for quick validation.

Citation

If you find our work useful for your research, please consider citing our paper. Thank you!

@inproceedings{2025CVPR_UniCD,
    title     = {Detection-Friendly Nonuniformity Correction: A Union Framework for Infrared {UAV} Target Detection},
    author    = {Houzhang Fang and Xiaolin Wang and Zengyang Li and Lu Wang and Qingshan Li and Yi Chang and Luxin Yan},
    booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year      = {2025},
    month     =  {June},
    pages     = {11898-11907},
}

In additoin to the above paper, please also consider citing the following references. Thank you!

@inproceedings{2026AAAI_TDCNet,
  title     = {Spatio-Temporal Context Learning with Temporal Difference Convolution for Moving Infrared Small Target Detection},
  author    = {Houzhang Fang and Shukai Guo and Qiuhuan Chen and Yi Chang and Luxin Yan},
  booktitle   = {Proceedings of the AAAI Conference on Artificial Intelligence},
  year      = {2026},
  pages     = {3840-3848},
}
@inproceedings{2026AAAI_JFD3,
  title     = {Blur-Robust Detection via Feature Restoration: An End-to-End Framework for Prior-Guided Infrared UAV Target Detection},
  author    = {Xiaolin Wang and Houzhang Fang and Qingshan Li and Lu Wang and Yi Chang and Luxin Yan},
  booktitle   = {Proceedings of the AAAI Conference on Artificial Intelligence},
  year      = {2026},
  pages     = {10181-10189},
}
@article{2024TGRS_SCINet,
  title     = {{SCINet}: Spatial and Contrast Interactive Super-Resolution Assisted Infrared {UAV} Target Detection},
  author    = {Houzhang Fang and Lan Ding and Xiaolin Wang and Yi Chang and Luxin Yan and Li Liu and Jinrui Fang},
  journal   = {IEEE Transactions on Geoscience and Remote Sensing},
  volume    = {62},
  year      = {2024},
  pages     = {1-22},
}
@ARTICLE{2023TII_DAGNet,
  title     =  {Differentiated Attention Guided Network Over Hierarchical and Aggregated Features for Intelligent {UAV} Surveillance},
  author    =  {Houzhang Fang and Zikai Liao and Xuhua Wang and Yi Chang and Luxin Yan},
  journal   =  {IEEE Transactions on Industrial Informatics}, 
  year      =  {2023},
  volume    =  {19},
  number    =  {9},
  pages     =  {9909-9920},
  }
@inproceedings{2023ACMMM_DANet,
title       =  {{DANet}: Multi-scale {UAV} Target Detection with Dynamic Feature Perception and Scale-aware Knowledge Distillation},
author      =  {Houzhang Fang and Zikai Liao and Lu Wang and Qingshan Li and Yi Chang and Luxin Yan and Xuhua Wang},
booktitle   =  {Proceedings of the 31st ACM International Conference on Multimedia (ACMMM)},
pages       =  {2121-2130},
year        =  {2023},
}
@ARTICLE{2022TIMFang,
  title     =  {Infrared Small {UAV} Target Detection Based on Depthwise Separable Residual Dense Network and Multiscale Feature Fusion},
  author    =  {Houzhang Fang and Lan Ding and Liming Wang and Yi Chang and Luxin Yan and Jinhui Han},
  journal   =  {IEEE Transactions on Instrumentation and Measurement}, 
  year      =  {2022},
  volume    =  {71},
  number    =  {},
  pages     =  {1-20},
}

Contact

If you have any question, please contact: houzhangfang@xidian.edu.cn and wxl@stu.xidian.edu.cn,

Copyright © Xidian University.

License

MIT License. This software is only freely available for non-commercial research use.