Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection
Detecting small targets in infrared images presents significant challenges due to their tiny size and complex backgrounds, making this task a hotspot for research. Traditional methods rely on assumption-based modeling and manual design, struggling to handle the variability of real-world scenarios. A...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/3/428 |
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| author | Yue Wang Xinhong Wang Shi Qiu Xianghui Chen Zhaoyan Liu Chuncheng Zhou Weiyuan Yao Hongjia Cheng Yu Zhang Feihong Wang Zhan Shu |
| author_facet | Yue Wang Xinhong Wang Shi Qiu Xianghui Chen Zhaoyan Liu Chuncheng Zhou Weiyuan Yao Hongjia Cheng Yu Zhang Feihong Wang Zhan Shu |
| author_sort | Yue Wang |
| collection | DOAJ |
| description | Detecting small targets in infrared images presents significant challenges due to their tiny size and complex backgrounds, making this task a hotspot for research. Traditional methods rely on assumption-based modeling and manual design, struggling to handle the variability of real-world scenarios. Although convolutional neural networks (CNNs) increase robustness to diverse scenes with a data-driven paradigm, many CNN-based methods are insufficient in capturing fine-grained details necessary for small targets and are less effective during multi-scale feature fusion. To overcome these challenges, we propose the novel Wide-scale Gated Fully Fusion Network (WGFFNet) in this article, which contributes to infrared small-target detection (IRSTD). WGFFNet uses a classic encoder–decoder structure, where the designed stepped fusion block (SFB) embedded in the feature extraction stage captures finer local context across multiple scales during encoding, and along the decoding path, the multi-level features are progressively integrated by a Fully Gated Interaction (FGI) Module to enhance feature representation. The inclusion of a boundary difference loss further optimizes the edge details of targets. We conducted comprehensive experiments on two public infrared small-target datasets: SIRST-V2 and IRSTD-1k. Quantitative and qualitative results demonstrate that our WGFFNet outperforms representative methods when considering various evaluation metrics together, achieving an improved detection performance and computational efficiency for detecting small targets in infrared images. |
| format | Article |
| id | doaj-art-1841f26c770047cc8ca05164a3b1ff2b |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-1841f26c770047cc8ca05164a3b1ff2b2025-08-20T02:12:29ZengMDPI AGRemote Sensing2072-42922025-01-0117342810.3390/rs17030428Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target DetectionYue Wang0Xinhong Wang1Shi Qiu2Xianghui Chen3Zhaoyan Liu4Chuncheng Zhou5Weiyuan Yao6Hongjia Cheng7Yu Zhang8Feihong Wang9Zhan Shu10Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaDetecting small targets in infrared images presents significant challenges due to their tiny size and complex backgrounds, making this task a hotspot for research. Traditional methods rely on assumption-based modeling and manual design, struggling to handle the variability of real-world scenarios. Although convolutional neural networks (CNNs) increase robustness to diverse scenes with a data-driven paradigm, many CNN-based methods are insufficient in capturing fine-grained details necessary for small targets and are less effective during multi-scale feature fusion. To overcome these challenges, we propose the novel Wide-scale Gated Fully Fusion Network (WGFFNet) in this article, which contributes to infrared small-target detection (IRSTD). WGFFNet uses a classic encoder–decoder structure, where the designed stepped fusion block (SFB) embedded in the feature extraction stage captures finer local context across multiple scales during encoding, and along the decoding path, the multi-level features are progressively integrated by a Fully Gated Interaction (FGI) Module to enhance feature representation. The inclusion of a boundary difference loss further optimizes the edge details of targets. We conducted comprehensive experiments on two public infrared small-target datasets: SIRST-V2 and IRSTD-1k. Quantitative and qualitative results demonstrate that our WGFFNet outperforms representative methods when considering various evaluation metrics together, achieving an improved detection performance and computational efficiency for detecting small targets in infrared images.https://www.mdpi.com/2072-4292/17/3/428infrared small-target detectionfeature fusiongate mechanismhierarchical decoding |
| spellingShingle | Yue Wang Xinhong Wang Shi Qiu Xianghui Chen Zhaoyan Liu Chuncheng Zhou Weiyuan Yao Hongjia Cheng Yu Zhang Feihong Wang Zhan Shu Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection Remote Sensing infrared small-target detection feature fusion gate mechanism hierarchical decoding |
| title | Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection |
| title_full | Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection |
| title_fullStr | Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection |
| title_full_unstemmed | Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection |
| title_short | Multi-Scale Hierarchical Feature Fusion for Infrared Small-Target Detection |
| title_sort | multi scale hierarchical feature fusion for infrared small target detection |
| topic | infrared small-target detection feature fusion gate mechanism hierarchical decoding |
| url | https://www.mdpi.com/2072-4292/17/3/428 |
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