SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target Detection
Infrared small target detection (IRSTD) is the process of recognizing and distinguishing small targets from infrared images that are obstructed by crowded backgrounds. This technique is used in various areas, including ground monitoring, flight navigation, and so on. However, due to complex backgrou...
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/22/4160 |
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| author | Zhihui Yu Nian Pan Jin Zhou |
| author_facet | Zhihui Yu Nian Pan Jin Zhou |
| author_sort | Zhihui Yu |
| collection | DOAJ |
| description | Infrared small target detection (IRSTD) is the process of recognizing and distinguishing small targets from infrared images that are obstructed by crowded backgrounds. This technique is used in various areas, including ground monitoring, flight navigation, and so on. However, due to complex backgrounds and the loss of information in deep networks, infrared small target detection remains a difficult undertaking. To solve the above problems, we present a shallow feature fusion network (SFFNet) based on detection framework. Specifically, we design the shallow-layer-guided feature enhancement (SLGFE) module, which guides multi-scale feature fusion with shallow layer information, effectively mitigating the loss of information in deep networks. Then, we design the visual-Mamba-based global information extension (VMamba-GIE) module, which leverages a multi-branch structure combining the capability of convolutional layers to extract features in local space with the advantages of state space models in the exploration of long-distance information. The design significantly extends the network’s capacity to acquire global contextual information, enhancing its capability to handle complex backgrounds. And through the effective fusion of the SLGFE and VMamba-GIE modules, the exorbitant computation brought by the SLGFE module is substantially reduced. The experimental results on two publicly available infrared small target datasets demonstrate that the SFFNet surpasses other state-of-the-art algorithms. |
| format | Article |
| id | doaj-art-2d32158d14a44c1baa8a22e76ed2801d |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-2d32158d14a44c1baa8a22e76ed2801d2025-08-20T02:05:07ZengMDPI AGRemote Sensing2072-42922024-11-011622416010.3390/rs16224160SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target DetectionZhihui Yu0Nian Pan1Jin Zhou2National Laboratory on Adaptive Optics, Chengdu 610209, ChinaNational Laboratory on Adaptive Optics, Chengdu 610209, ChinaNational Laboratory on Adaptive Optics, Chengdu 610209, ChinaInfrared small target detection (IRSTD) is the process of recognizing and distinguishing small targets from infrared images that are obstructed by crowded backgrounds. This technique is used in various areas, including ground monitoring, flight navigation, and so on. However, due to complex backgrounds and the loss of information in deep networks, infrared small target detection remains a difficult undertaking. To solve the above problems, we present a shallow feature fusion network (SFFNet) based on detection framework. Specifically, we design the shallow-layer-guided feature enhancement (SLGFE) module, which guides multi-scale feature fusion with shallow layer information, effectively mitigating the loss of information in deep networks. Then, we design the visual-Mamba-based global information extension (VMamba-GIE) module, which leverages a multi-branch structure combining the capability of convolutional layers to extract features in local space with the advantages of state space models in the exploration of long-distance information. The design significantly extends the network’s capacity to acquire global contextual information, enhancing its capability to handle complex backgrounds. And through the effective fusion of the SLGFE and VMamba-GIE modules, the exorbitant computation brought by the SLGFE module is substantially reduced. The experimental results on two publicly available infrared small target datasets demonstrate that the SFFNet surpasses other state-of-the-art algorithms.https://www.mdpi.com/2072-4292/16/22/4160infrared small target detectionmultiscale features fusionstate space modelsmambadetection framework |
| spellingShingle | Zhihui Yu Nian Pan Jin Zhou SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target Detection Remote Sensing infrared small target detection multiscale features fusion state space models mamba detection framework |
| title | SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target Detection |
| title_full | SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target Detection |
| title_fullStr | SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target Detection |
| title_full_unstemmed | SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target Detection |
| title_short | SFFNet: Shallow Feature Fusion Network Based on Detection Framework for Infrared Small Target Detection |
| title_sort | sffnet shallow feature fusion network based on detection framework for infrared small target detection |
| topic | infrared small target detection multiscale features fusion state space models mamba detection framework |
| url | https://www.mdpi.com/2072-4292/16/22/4160 |
| work_keys_str_mv | AT zhihuiyu sffnetshallowfeaturefusionnetworkbasedondetectionframeworkforinfraredsmalltargetdetection AT nianpan sffnetshallowfeaturefusionnetworkbasedondetectionframeworkforinfraredsmalltargetdetection AT jinzhou sffnetshallowfeaturefusionnetworkbasedondetectionframeworkforinfraredsmalltargetdetection |