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IRE-YOLO: Infrared weak target detection algorithm based on the fusion of multi-scale receptive fields and efficient convolution

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A Correction to this article was published on 01 April 2025

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Abstract

In recent years, the technology for detecting small and dim infrared targets has played a crucial role in both military and civilian security fields. Deep learning-based methods have also achieved remarkable progress in this area. However, it is still restricted by challenges such as small target size, low signal-to-noise ratio, and complex backgrounds. Therefore, this paper proposes an improved model IRE-YOLO based on You Only Look Once (YOLO) to enhance the detection accuracy of small targets. To improve the model’s feature extraction ability for targets, a receptive field enhancement module based on dilated convolution and shared weights is proposed. By expanding the receptive field of the feature map, it can extract the detailed features and local information of multi-scale targets. Secondly, to address the difficulties of small target size and low image resolution, a space-to-depth convolution is added to the backbone network. By converting the spatial dimension into the depth dimension, it can effectively capture the context information of small targets. In addition, to enhance the accuracy of the model for real detection boxes, this paper proposes an SNS algorithm, which can effectively remove redundant detection boxes. IRE-YOLO is compared and evaluated with other models on two public datasets, IDTA and SIRST. The experimental results show that compared with the baseline YOLOv5s, the mean average precision (mAP) of IRE-YOLO has increased by 2% and 2.1%, respectively, significantly improving the detection accuracy of small and dim infrared targets.

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Data Availability

No datasets were generated or analysed during the current study.

Change history

  • 27 March 2025

    The original online version of this article was revised: " The Funding information section was missing from this article and should have read '1. National Natural Science Foundation of China (Grant No. 62261004) 2. Guangxi Distinguished Young Scholars Research Fund (Grant No. 2025GXNSFFA069014) 3. Guangxi Distinguished Young Scholars Research Fund (Grant No. 2023GXNSFFA026002)'".

  • 01 April 2025

    A Correction to this paper has been published: https://doi.org/10.1007/s11227-025-07223-9

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Funding

National Natural Science Foundation of China (Grant No. 62261004, Guangxi Distinguished Young Scholars Research Fund (Grant No. 2025GXNSFFA069014), Guangxi Distinguished Young Scholars Research Fund (Grant No. 2023GXNSFFA026002).

Author information

Authors and Affiliations

  1. School of Automation, Guangxi University of Science and Technology, No. 19, Guantang Avenue, Yufeng District, Liuzhou, 545006, Guangxi, China

    Qingxiao Ma, Xiangsuo Fan & Tingting Li

  2. School of Electronic Engineering, Guangxi University of Science and Technology, No. 268, Donghuan Avenue, Chengzhong District, Liuzhou, 545006, Guangxi, China

    Huajin Chen

Authors
  1. Qingxiao Ma
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  2. Xiangsuo Fan
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Contributions

Ma: Undertook the primary writing of the first draft of the dissertation. Organically integrated the research results from various sections and presented them in a clear and logically coherent manner. Carefully organized the structure of the paper, including various sections such as abstract, introduction, methods, results, discussion and conclusion, to ensure that the paper is complete and well-organized. Fan: Took the lead in planning the entire study, presenting the research questions and general ideas. Based on the current status and potential research gaps in the research area, research directions and objectives were defined, and a framework was set up for subsequent research work. Chen: After the completion of the first draft of the thesis, the language expression, logical clarity and formatting specifications of the thesis were meticulously checked and embellished. We improved the readability of the thesis in terms of grammar, vocabulary and sentence structure, and made many constructive suggestions for revision, which made the thesis more accurate and concise in expression. Li：Mainly responsible for the drawing of tables and pictures throughout the paper.

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Correspondence to Xiangsuo Fan.

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The original online version of this article was revised: " The Funding information section was missing from this article and should have read '1. National Natural Science Foundation of China (Grant No. 62261004) 2. Guangxi Distinguished Young Scholars Research Fund (Grant No. 2025GXNSFFA069014) 3. Guangxi Distinguished Young Scholars Research Fund (Grant No. 2023GXNSFFA026002)'".

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Ma, Q., Fan, X., Chen, H. et al. IRE-YOLO: Infrared weak target detection algorithm based on the fusion of multi-scale receptive fields and efficient convolution. J Supercomput 81, 558 (2025). https://doi.org/10.1007/s11227-025-07057-5

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