Infrared Small Target Detection Based on Weak Feature Enhancement and Target Adaptive Proliferation
The imbalance between positive and negative samples and the loss of small targets in complex backgrounds are catastrophic for infrared small target detection. To address these issues, we proposed an infrared small target detection method based on weak feature enhancement and target adaptive prolifer...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10772282/ |
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| author | Xiaoyu Xu Weida Zhan Yichun Jiang Depeng Zhu Yu Chen Jinxin Guo Ziqiang Hao Deng Han |
| author_facet | Xiaoyu Xu Weida Zhan Yichun Jiang Depeng Zhu Yu Chen Jinxin Guo Ziqiang Hao Deng Han |
| author_sort | Xiaoyu Xu |
| collection | DOAJ |
| description | The imbalance between positive and negative samples and the loss of small targets in complex backgrounds are catastrophic for infrared small target detection. To address these issues, we proposed an infrared small target detection method based on weak feature enhancement and target adaptive proliferation (IRSTD-WFETAP). First, we utilized a sparse sampling mechanism and hybrid filtering method to flexibly capture the complex shapes and edge information of small targets while reducing the loss and shift of scarce features. Then, we introduced a multiscale feature enhancement module that used vertical-horizontal bidirectional attention and multiscale feature encoding to establish stable feature interaction channels between the encoder and decoder, further enhancing key features of small targets. In addition, we introduced a target data self-adaptive proliferation strategy (DSAS) to address the imbalance of positive and negative samples, enhancing the generalization and expression capability of the detection datasets. Finally, we proposed a target-background joint loss to alleviate the imbalance issue and help the network converge smoothly. Extensive experiments on NUAA-SIRST, IRSTD-1k, and our custom-made dataset demonstrated the effectiveness of the proposed IRSTD-WFETAP method, achieving superior performance in nIoU, Pd, Fa, and F1-measure compared to the latest methods. |
| format | Article |
| id | doaj-art-2e55ebe4447643faa07d1f262e288aeb |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-2e55ebe4447643faa07d1f262e288aeb2025-08-20T01:47:25ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01182829285010.1109/JSTARS.2024.350999310772282Infrared Small Target Detection Based on Weak Feature Enhancement and Target Adaptive ProliferationXiaoyu Xu0https://orcid.org/0009-0003-2308-4198Weida Zhan1https://orcid.org/0000-0003-1011-7416Yichun Jiang2https://orcid.org/0000-0002-0178-9416Depeng Zhu3https://orcid.org/0000-0002-1227-539XYu Chen4Jinxin Guo5Ziqiang Hao6Deng Han7College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, ChinaCollege of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, ChinaCollege of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, ChinaCollege of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, ChinaCollege of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, ChinaCollege of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, ChinaCollege of Electronic Information Engineering, Changchun University of Science and Technology, Changchun, ChinaJilin Province Zhixing IoT Research Institute Company Ltd., Changchun, ChinaThe imbalance between positive and negative samples and the loss of small targets in complex backgrounds are catastrophic for infrared small target detection. To address these issues, we proposed an infrared small target detection method based on weak feature enhancement and target adaptive proliferation (IRSTD-WFETAP). First, we utilized a sparse sampling mechanism and hybrid filtering method to flexibly capture the complex shapes and edge information of small targets while reducing the loss and shift of scarce features. Then, we introduced a multiscale feature enhancement module that used vertical-horizontal bidirectional attention and multiscale feature encoding to establish stable feature interaction channels between the encoder and decoder, further enhancing key features of small targets. In addition, we introduced a target data self-adaptive proliferation strategy (DSAS) to address the imbalance of positive and negative samples, enhancing the generalization and expression capability of the detection datasets. Finally, we proposed a target-background joint loss to alleviate the imbalance issue and help the network converge smoothly. Extensive experiments on NUAA-SIRST, IRSTD-1k, and our custom-made dataset demonstrated the effectiveness of the proposed IRSTD-WFETAP method, achieving superior performance in nIoU, Pd, Fa, and F1-measure compared to the latest methods.https://ieeexplore.ieee.org/document/10772282/Deep learningthermal infrared imagethermal infrared small target detection (IRSTD)weak feature enhancement |
| spellingShingle | Xiaoyu Xu Weida Zhan Yichun Jiang Depeng Zhu Yu Chen Jinxin Guo Ziqiang Hao Deng Han Infrared Small Target Detection Based on Weak Feature Enhancement and Target Adaptive Proliferation IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning thermal infrared image thermal infrared small target detection (IRSTD) weak feature enhancement |
| title | Infrared Small Target Detection Based on Weak Feature Enhancement and Target Adaptive Proliferation |
| title_full | Infrared Small Target Detection Based on Weak Feature Enhancement and Target Adaptive Proliferation |
| title_fullStr | Infrared Small Target Detection Based on Weak Feature Enhancement and Target Adaptive Proliferation |
| title_full_unstemmed | Infrared Small Target Detection Based on Weak Feature Enhancement and Target Adaptive Proliferation |
| title_short | Infrared Small Target Detection Based on Weak Feature Enhancement and Target Adaptive Proliferation |
| title_sort | infrared small target detection based on weak feature enhancement and target adaptive proliferation |
| topic | Deep learning thermal infrared image thermal infrared small target detection (IRSTD) weak feature enhancement |
| url | https://ieeexplore.ieee.org/document/10772282/ |
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