Enhanced YOLOv10 Framework Featuring DPAM and DALSM for Real-Time Underwater Object Detection
Recently, spotting underwater objects has been increasingly difficult due to the complexities of marine environments and varied visibility conditions. YOLOv10 is notable for its effective, robust architecture, featuring significant components: advanced backbone networks and enhanced feature pyramid...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10833620/ |
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| author | Suthir Sriram Aburvan P. Arun Kaarthic T. P. Nivethitha Vijayaraj Thangavel Murugan |
| author_facet | Suthir Sriram Aburvan P. Arun Kaarthic T. P. Nivethitha Vijayaraj Thangavel Murugan |
| author_sort | Suthir Sriram |
| collection | DOAJ |
| description | Recently, spotting underwater objects has been increasingly difficult due to the complexities of marine environments and varied visibility conditions. YOLOv10 is notable for its effective, robust architecture, featuring significant components: advanced backbone networks and enhanced feature pyramid networks that also deliver anchor-free detection. In delivering YOLOv10, we enhance it with dual partial attention mechanism (DPAM) and dual adaptive label assignment with sun glint removal module (DALSM) along with marine fusion loss (MFL). With DPAM, the latest refinement processes for conservation focus on feature extraction to account for key highlights and in the scene, include temporal context, both critical for interpretation of the dynamic realm below the ocean. The prefix with DALSM involves adaptive dual label assignment and techniques for sun glint removal. The marine fusion loss (MFL) provides an object detection prediction that combines both binary cross-entropy loss and complete intersection over union (CIoU) loss to enhance bounding box localization while also including spatial context to incorporate important underwater features. With the experiments, we attenuate enhancements with device gradient clipping, model checkpointing, and advanced augmentation processes to gain 3.04% improvement in mean Average Precision (mAP). These enhancements mitigate perceived challenges of underwater detection while enhancing knowledge of understanding marine life. |
| format | Article |
| id | doaj-art-56b270171e92492c8f0b5697f8435c91 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-56b270171e92492c8f0b5697f8435c912025-01-21T00:01:51ZengIEEEIEEE Access2169-35362025-01-01138691870810.1109/ACCESS.2025.352731510833620Enhanced YOLOv10 Framework Featuring DPAM and DALSM for Real-Time Underwater Object DetectionSuthir Sriram0https://orcid.org/0000-0003-2480-3273Aburvan P.1https://orcid.org/0009-0008-4850-3009Arun Kaarthic T. P.2https://orcid.org/0009-0003-6610-4507Nivethitha Vijayaraj3https://orcid.org/0000-0002-8694-033XThangavel Murugan4https://orcid.org/0000-0002-2510-8857Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, IndiaDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, IndiaDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, IndiaDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, IndiaCollege of Information and Technology, United Arab Emirates University, Al Ain, United Arab EmiratesRecently, spotting underwater objects has been increasingly difficult due to the complexities of marine environments and varied visibility conditions. YOLOv10 is notable for its effective, robust architecture, featuring significant components: advanced backbone networks and enhanced feature pyramid networks that also deliver anchor-free detection. In delivering YOLOv10, we enhance it with dual partial attention mechanism (DPAM) and dual adaptive label assignment with sun glint removal module (DALSM) along with marine fusion loss (MFL). With DPAM, the latest refinement processes for conservation focus on feature extraction to account for key highlights and in the scene, include temporal context, both critical for interpretation of the dynamic realm below the ocean. The prefix with DALSM involves adaptive dual label assignment and techniques for sun glint removal. The marine fusion loss (MFL) provides an object detection prediction that combines both binary cross-entropy loss and complete intersection over union (CIoU) loss to enhance bounding box localization while also including spatial context to incorporate important underwater features. With the experiments, we attenuate enhancements with device gradient clipping, model checkpointing, and advanced augmentation processes to gain 3.04% improvement in mean Average Precision (mAP). These enhancements mitigate perceived challenges of underwater detection while enhancing knowledge of understanding marine life.https://ieeexplore.ieee.org/document/10833620/Dual partial attention mechanism (DPAM)dual adaptive label assignment with sun glint removal module (DALSM)marine-fusion loss (MFL)YOLOv10 |
| spellingShingle | Suthir Sriram Aburvan P. Arun Kaarthic T. P. Nivethitha Vijayaraj Thangavel Murugan Enhanced YOLOv10 Framework Featuring DPAM and DALSM for Real-Time Underwater Object Detection IEEE Access Dual partial attention mechanism (DPAM) dual adaptive label assignment with sun glint removal module (DALSM) marine-fusion loss (MFL) YOLOv10 |
| title | Enhanced YOLOv10 Framework Featuring DPAM and DALSM for Real-Time Underwater Object Detection |
| title_full | Enhanced YOLOv10 Framework Featuring DPAM and DALSM for Real-Time Underwater Object Detection |
| title_fullStr | Enhanced YOLOv10 Framework Featuring DPAM and DALSM for Real-Time Underwater Object Detection |
| title_full_unstemmed | Enhanced YOLOv10 Framework Featuring DPAM and DALSM for Real-Time Underwater Object Detection |
| title_short | Enhanced YOLOv10 Framework Featuring DPAM and DALSM for Real-Time Underwater Object Detection |
| title_sort | enhanced yolov10 framework featuring dpam and dalsm for real time underwater object detection |
| topic | Dual partial attention mechanism (DPAM) dual adaptive label assignment with sun glint removal module (DALSM) marine-fusion loss (MFL) YOLOv10 |
| url | https://ieeexplore.ieee.org/document/10833620/ |
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