Polarization-Enhanced Multi-Target Underwater Salient Object Detection
Salient object detection (SOD) plays a critical role in underwater exploration systems. Traditional SOD approaches encounter notable constraints in underwater image analysis, primarily stemming from light scattering and absorption effects induced by suspended particulate matter in complex underwater...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/12/7/707 |
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| author | Jiayi Song Peikai Zhao Jiangtao Li Liming Zhu Khian-Hooi Chew Rui-Pin Chen |
| author_facet | Jiayi Song Peikai Zhao Jiangtao Li Liming Zhu Khian-Hooi Chew Rui-Pin Chen |
| author_sort | Jiayi Song |
| collection | DOAJ |
| description | Salient object detection (SOD) plays a critical role in underwater exploration systems. Traditional SOD approaches encounter notable constraints in underwater image analysis, primarily stemming from light scattering and absorption effects induced by suspended particulate matter in complex underwater environments. In this work, we propose a deep learning-based multimodal method guided by multi-polarization parameters that integrates polarization de-scattering mechanisms with the powerful feature learning capability of neural networks to achieve adaptive multi-target SOD in an underwater turbid scattering environment. The proposed polarization-enhanced salient object detection network (PESODNet) employs a multi-polarization-parameter-guided, material-aware attention mechanism and a contrastive feature calibration unit, significantly enhancing its multi-material, multi-target detection capabilities in underwater scattering environments. The experimental results confirm that the proposed method achieves substantial performance improvements in multi-target underwater SOD tasks, outperforming state-of-the-art models of salient object detection in detection accuracy. |
| format | Article |
| id | doaj-art-6c8f13622b0042fb8ee799015d664588 |
| institution | DOAJ |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj-art-6c8f13622b0042fb8ee799015d6645882025-08-20T03:08:13ZengMDPI AGPhotonics2304-67322025-07-0112770710.3390/photonics12070707Polarization-Enhanced Multi-Target Underwater Salient Object DetectionJiayi Song0Peikai Zhao1Jiangtao Li2Liming Zhu3Khian-Hooi Chew4Rui-Pin Chen5Key Laboratory of Optical Field Manipulation of Zhejiang Province, Department of Physics, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaKey Laboratory of Optical Field Manipulation of Zhejiang Province, Department of Physics, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaKey Laboratory of Optical Field Manipulation of Zhejiang Province, Department of Physics, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaKey Laboratory of Optical Field Manipulation of Zhejiang Province, Department of Physics, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaKey Laboratory of Optical Field Manipulation of Zhejiang Province, Department of Physics, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaKey Laboratory of Optical Field Manipulation of Zhejiang Province, Department of Physics, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSalient object detection (SOD) plays a critical role in underwater exploration systems. Traditional SOD approaches encounter notable constraints in underwater image analysis, primarily stemming from light scattering and absorption effects induced by suspended particulate matter in complex underwater environments. In this work, we propose a deep learning-based multimodal method guided by multi-polarization parameters that integrates polarization de-scattering mechanisms with the powerful feature learning capability of neural networks to achieve adaptive multi-target SOD in an underwater turbid scattering environment. The proposed polarization-enhanced salient object detection network (PESODNet) employs a multi-polarization-parameter-guided, material-aware attention mechanism and a contrastive feature calibration unit, significantly enhancing its multi-material, multi-target detection capabilities in underwater scattering environments. The experimental results confirm that the proposed method achieves substantial performance improvements in multi-target underwater SOD tasks, outperforming state-of-the-art models of salient object detection in detection accuracy.https://www.mdpi.com/2304-6732/12/7/707deep learningpolarization imagingunderwater salient object detection |
| spellingShingle | Jiayi Song Peikai Zhao Jiangtao Li Liming Zhu Khian-Hooi Chew Rui-Pin Chen Polarization-Enhanced Multi-Target Underwater Salient Object Detection Photonics deep learning polarization imaging underwater salient object detection |
| title | Polarization-Enhanced Multi-Target Underwater Salient Object Detection |
| title_full | Polarization-Enhanced Multi-Target Underwater Salient Object Detection |
| title_fullStr | Polarization-Enhanced Multi-Target Underwater Salient Object Detection |
| title_full_unstemmed | Polarization-Enhanced Multi-Target Underwater Salient Object Detection |
| title_short | Polarization-Enhanced Multi-Target Underwater Salient Object Detection |
| title_sort | polarization enhanced multi target underwater salient object detection |
| topic | deep learning polarization imaging underwater salient object detection |
| url | https://www.mdpi.com/2304-6732/12/7/707 |
| work_keys_str_mv | AT jiayisong polarizationenhancedmultitargetunderwatersalientobjectdetection AT peikaizhao polarizationenhancedmultitargetunderwatersalientobjectdetection AT jiangtaoli polarizationenhancedmultitargetunderwatersalientobjectdetection AT limingzhu polarizationenhancedmultitargetunderwatersalientobjectdetection AT khianhooichew polarizationenhancedmultitargetunderwatersalientobjectdetection AT ruipinchen polarizationenhancedmultitargetunderwatersalientobjectdetection |