Multi-Scale Feature Fusion Enhancement for Underwater Object Detection
Underwater object detection (UOD) presents substantial challenges due to the complex visual conditions and the physical properties of light in underwater environments. Small aquatic creatures often congregate in large groups, further complicating the task. To address these challenges, we develop Aqu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/22/7201 |
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| author | Zhanhao Xiao Zhenpeng Li Huihui Li Mengting Li Xiaoyong Liu Yinying Kong |
| author_facet | Zhanhao Xiao Zhenpeng Li Huihui Li Mengting Li Xiaoyong Liu Yinying Kong |
| author_sort | Zhanhao Xiao |
| collection | DOAJ |
| description | Underwater object detection (UOD) presents substantial challenges due to the complex visual conditions and the physical properties of light in underwater environments. Small aquatic creatures often congregate in large groups, further complicating the task. To address these challenges, we develop Aqua-DETR, a tailored end-to-end framework for UOD. Our method includes an align-split network to enhance multi-scale feature interaction and fusion for small object identification and a distinction enhancement module using various attention mechanisms to improve ambiguous object identification. Experimental results on four challenging datasets demonstrate that Aqua-DETR outperforms most existing state-of-the-art methods in the UOD task, validating its effectiveness and robustness. |
| format | Article |
| id | doaj-art-77d2cd18c6ae4b41b5b8cd359863f34c |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-77d2cd18c6ae4b41b5b8cd359863f34c2024-11-26T18:21:05ZengMDPI AGSensors1424-82202024-11-012422720110.3390/s24227201Multi-Scale Feature Fusion Enhancement for Underwater Object DetectionZhanhao Xiao0Zhenpeng Li1Huihui Li2Mengting Li3Xiaoyong Liu4Yinying Kong5School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaSchool of Data Science and Engineering, Guangdong Polytechnic Normal University, Heyuan 517583, ChinaSchool of Data Science and Engineering, Guangdong Polytechnic Normal University, Heyuan 517583, ChinaSchool of Statistics and Mathematics, Guangdong University of Finance & Economics, Guangzhou 510320, ChinaUnderwater object detection (UOD) presents substantial challenges due to the complex visual conditions and the physical properties of light in underwater environments. Small aquatic creatures often congregate in large groups, further complicating the task. To address these challenges, we develop Aqua-DETR, a tailored end-to-end framework for UOD. Our method includes an align-split network to enhance multi-scale feature interaction and fusion for small object identification and a distinction enhancement module using various attention mechanisms to improve ambiguous object identification. Experimental results on four challenging datasets demonstrate that Aqua-DETR outperforms most existing state-of-the-art methods in the UOD task, validating its effectiveness and robustness.https://www.mdpi.com/1424-8220/24/22/7201underwater object detectionDETRcross-scale feature fusion |
| spellingShingle | Zhanhao Xiao Zhenpeng Li Huihui Li Mengting Li Xiaoyong Liu Yinying Kong Multi-Scale Feature Fusion Enhancement for Underwater Object Detection Sensors underwater object detection DETR cross-scale feature fusion |
| title | Multi-Scale Feature Fusion Enhancement for Underwater Object Detection |
| title_full | Multi-Scale Feature Fusion Enhancement for Underwater Object Detection |
| title_fullStr | Multi-Scale Feature Fusion Enhancement for Underwater Object Detection |
| title_full_unstemmed | Multi-Scale Feature Fusion Enhancement for Underwater Object Detection |
| title_short | Multi-Scale Feature Fusion Enhancement for Underwater Object Detection |
| title_sort | multi scale feature fusion enhancement for underwater object detection |
| topic | underwater object detection DETR cross-scale feature fusion |
| url | https://www.mdpi.com/1424-8220/24/22/7201 |
| work_keys_str_mv | AT zhanhaoxiao multiscalefeaturefusionenhancementforunderwaterobjectdetection AT zhenpengli multiscalefeaturefusionenhancementforunderwaterobjectdetection AT huihuili multiscalefeaturefusionenhancementforunderwaterobjectdetection AT mengtingli multiscalefeaturefusionenhancementforunderwaterobjectdetection AT xiaoyongliu multiscalefeaturefusionenhancementforunderwaterobjectdetection AT yinyingkong multiscalefeaturefusionenhancementforunderwaterobjectdetection |