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IR-SAM2: Target Enhancement with SAM2 for Infrared Small Target Detection.

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Title: IR-SAM2: Target Enhancement with SAM2 for Infrared Small Target Detection.
Authors: Hao, Zongduo1 (AUTHOR), Dang, Xiaocui1,2 (AUTHOR), Zhang, Yanyu2,3 (AUTHOR), Miao, Jinshui1,3 (AUTHOR), Li, Zhiming2,3 (AUTHOR), Lu, Xiankai1,3 (AUTHOR) luxiankai@sdu.edu.cn
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p1891. 24p.
Subjects: Image segmentation, Signal detection, Machine learning, Image enhancement (Imaging systems)
Abstract: Highlights: What are the main findings? IR-SAM2 introduces a novel target enhancement framework for infrared small target detection (IRSTD) by integrating a Contrast Query Generator (CQG) and a Radial High-Pass Modulator (RHPM), effectively incorporating frequency domain context to suppress low-frequency background clutter and enhance small target saliency. IR-SAM2 achieves state-of-the-art performance on IRSTD-1k (IoU: 69.75%, P d : 96.60%) and NUDT-SIRST (IoU: 94.18%, P d : 98.94%), while striking an optimal balance between P d and F a on NUAA-SIRST, demonstrating superior robustness in complex backgrounds. What are the implications of the main findings? This work validates the feasibility of adapting vision foundation models (SAM2) to specialized infrared tasks by bridging the domain gap through spatio-frequency learning, offering a new paradigm for high-precision segmentation of extremely small targets with sparse visual cues. The proposed target-centric CSA loss and frequency driven optimization provide a robust and transferable solution for real-world infrared monitoring systems, such as maritime surveillance and airspace early warning, where distinguishing dim targets from heavy low-frequency interference is a critical requirement. Foundation models such as the Segment Anything Model (SAM) have substantially advanced promptable object segmentation in remote sensing. However, extending these capabilities to infrared small target detection (IRSTD) remains highly challenging in the presence of severe background clutter and extremely low target visibility. In this paper, we propose IR-SAM2, an effective target enhancement framework for mask-level infrared small target segmentation in the IRSTD setting. Specifically, IR-SAM2 equips the SAM2 decoder with a dedicated frequency branch, facilitating simultaneous spatio-frequency learning and deep spatio-frequency fusion, while preserving SAM2's pre-trained knowledge. Moreover, we introduce a target-centric loss to better guide the model in distinguishing small targets from complex backgrounds. Extensive experiments show that IR-SAM2 achieves highly competitive performance on the IRSTD-1k and NUDT-SIRST benchmarks, while striking an optimal balance between detection probability and false alarm rate on NUAA-SIRST. The results further demonstrate the effectiveness of spatio-frequency cues for complex-scene infrared small target segmentation. The source codes have been made publicly available to support reproducibility. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: IR-SAM2: Target Enhancement with SAM2 for Infrared Small Target Detection.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1891. 24p.
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  Data: Highlights: What are the main findings? IR-SAM2 introduces a novel target enhancement framework for infrared small target detection (IRSTD) by integrating a Contrast Query Generator (CQG) and a Radial High-Pass Modulator (RHPM), effectively incorporating frequency domain context to suppress low-frequency background clutter and enhance small target saliency. IR-SAM2 achieves state-of-the-art performance on IRSTD-1k (IoU: 69.75%, P d : 96.60%) and NUDT-SIRST (IoU: 94.18%, P d : 98.94%), while striking an optimal balance between P d and F a on NUAA-SIRST, demonstrating superior robustness in complex backgrounds. What are the implications of the main findings? This work validates the feasibility of adapting vision foundation models (SAM2) to specialized infrared tasks by bridging the domain gap through spatio-frequency learning, offering a new paradigm for high-precision segmentation of extremely small targets with sparse visual cues. The proposed target-centric CSA loss and frequency driven optimization provide a robust and transferable solution for real-world infrared monitoring systems, such as maritime surveillance and airspace early warning, where distinguishing dim targets from heavy low-frequency interference is a critical requirement. Foundation models such as the Segment Anything Model (SAM) have substantially advanced promptable object segmentation in remote sensing. However, extending these capabilities to infrared small target detection (IRSTD) remains highly challenging in the presence of severe background clutter and extremely low target visibility. In this paper, we propose IR-SAM2, an effective target enhancement framework for mask-level infrared small target segmentation in the IRSTD setting. Specifically, IR-SAM2 equips the SAM2 decoder with a dedicated frequency branch, facilitating simultaneous spatio-frequency learning and deep spatio-frequency fusion, while preserving SAM2's pre-trained knowledge. Moreover, we introduce a target-centric loss to better guide the model in distinguishing small targets from complex backgrounds. Extensive experiments show that IR-SAM2 achieves highly competitive performance on the IRSTD-1k and NUDT-SIRST benchmarks, while striking an optimal balance between detection probability and false alarm rate on NUAA-SIRST. The results further demonstrate the effectiveness of spatio-frequency cues for complex-scene infrared small target segmentation. The source codes have been made publicly available to support reproducibility. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18121891
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        Text: English
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      – SubjectFull: Signal detection
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Image enhancement (Imaging systems)
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      – TitleFull: IR-SAM2: Target Enhancement with SAM2 for Infrared Small Target Detection.
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            NameFull: Hao, Zongduo
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              M: 06
              Text: Jun2026
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              Y: 2026
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