MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection
Infrared small-target detection encounters significant challenges due to a low image signal-to-noise ratio, limited target size, and complex background noise. To address the issues of sparse feature loss for small targets during the down-sampling phase of the traditional U-Net network and the semant...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/14/2502 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849732927922896896 |
|---|---|
| author | Jingcui Ma Nian Pan Dengyu Yin Di Wang Jin Zhou |
| author_facet | Jingcui Ma Nian Pan Dengyu Yin Di Wang Jin Zhou |
| author_sort | Jingcui Ma |
| collection | DOAJ |
| description | Infrared small-target detection encounters significant challenges due to a low image signal-to-noise ratio, limited target size, and complex background noise. To address the issues of sparse feature loss for small targets during the down-sampling phase of the traditional U-Net network and the semantic gap in the feature fusion process, a multilevel feature extraction and fusion attention network (MEFA-Net) is designed. Specifically, the dilated direction-sensitive convolution block (DDCB) is devised to collaboratively extract local detail features, contextual features, and Gaussian salient features via ordinary convolution, dilated convolution and parallel strip convolution. Furthermore, the encoder attention fusion module (EAF) is employed, where spatial and channel attention weights are generated using dual-path pooling to achieve the adaptive fusion of deep and shallow layer features. Lastly, an efficient up-sampling block (EUB) is constructed, integrating a hybrid up-sampling strategy with multi-scale dilated convolution to refine the localization of small targets. The experimental results confirm that the proposed algorithm model surpasses most existing recent methods. Compared with the baseline, the intersection over union (IoU) and probability of detection <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>d</mi></mrow></msub></mrow></semantics></math></inline-formula> of MEFA-Net on the IRSTD-1k dataset are increased by 2.25% and 3.05%, respectively, achieving better detection performance and a lower false alarm rate in complex scenarios. |
| format | Article |
| id | doaj-art-604720e877bf4747a2f5a13b6f4cd2b2 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-604720e877bf4747a2f5a13b6f4cd2b22025-08-20T03:08:10ZengMDPI AGRemote Sensing2072-42922025-07-011714250210.3390/rs17142502MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target DetectionJingcui Ma0Nian Pan1Dengyu Yin2Di Wang3Jin Zhou4National Laboratory on Adaptive Optics, Chengdu 610209, ChinaNational Laboratory on Adaptive Optics, Chengdu 610209, ChinaAVIC Chengdu Aircraft Design & Research Institute, Chengdu 610091, ChinaAVIC Chengdu Aircraft Design & Research Institute, Chengdu 610091, ChinaNational Laboratory on Adaptive Optics, Chengdu 610209, ChinaInfrared small-target detection encounters significant challenges due to a low image signal-to-noise ratio, limited target size, and complex background noise. To address the issues of sparse feature loss for small targets during the down-sampling phase of the traditional U-Net network and the semantic gap in the feature fusion process, a multilevel feature extraction and fusion attention network (MEFA-Net) is designed. Specifically, the dilated direction-sensitive convolution block (DDCB) is devised to collaboratively extract local detail features, contextual features, and Gaussian salient features via ordinary convolution, dilated convolution and parallel strip convolution. Furthermore, the encoder attention fusion module (EAF) is employed, where spatial and channel attention weights are generated using dual-path pooling to achieve the adaptive fusion of deep and shallow layer features. Lastly, an efficient up-sampling block (EUB) is constructed, integrating a hybrid up-sampling strategy with multi-scale dilated convolution to refine the localization of small targets. The experimental results confirm that the proposed algorithm model surpasses most existing recent methods. Compared with the baseline, the intersection over union (IoU) and probability of detection <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>d</mi></mrow></msub></mrow></semantics></math></inline-formula> of MEFA-Net on the IRSTD-1k dataset are increased by 2.25% and 3.05%, respectively, achieving better detection performance and a lower false alarm rate in complex scenarios.https://www.mdpi.com/2072-4292/17/14/2502infrared small-target detectiondilated direction-sensitive convolutionencoder attention fusionefficient up-sampling |
| spellingShingle | Jingcui Ma Nian Pan Dengyu Yin Di Wang Jin Zhou MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection Remote Sensing infrared small-target detection dilated direction-sensitive convolution encoder attention fusion efficient up-sampling |
| title | MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection |
| title_full | MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection |
| title_fullStr | MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection |
| title_full_unstemmed | MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection |
| title_short | MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection |
| title_sort | mefa net multilevel feature extraction and fusion attention network for infrared small target detection |
| topic | infrared small-target detection dilated direction-sensitive convolution encoder attention fusion efficient up-sampling |
| url | https://www.mdpi.com/2072-4292/17/14/2502 |
| work_keys_str_mv | AT jingcuima mefanetmultilevelfeatureextractionandfusionattentionnetworkforinfraredsmalltargetdetection AT nianpan mefanetmultilevelfeatureextractionandfusionattentionnetworkforinfraredsmalltargetdetection AT dengyuyin mefanetmultilevelfeatureextractionandfusionattentionnetworkforinfraredsmalltargetdetection AT diwang mefanetmultilevelfeatureextractionandfusionattentionnetworkforinfraredsmalltargetdetection AT jinzhou mefanetmultilevelfeatureextractionandfusionattentionnetworkforinfraredsmalltargetdetection |