Lightweight marine biodetection model based on improved YOLOv10
In IoT-enabled marine biology, real-time monitoring of marine organisms faces challenges due to blurred images and complex underwater backgrounds, which hinder feature extraction and lead to missed detections. Addressing these issues, the lightweight YOLOv10-AD model introduces AKVanillaNet, a novel...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825001048 |
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| _version_ | 1825206968907726848 |
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| author | Wei Pan Jiabao Chen Bangjun Lv Likun Peng |
| author_facet | Wei Pan Jiabao Chen Bangjun Lv Likun Peng |
| author_sort | Wei Pan |
| collection | DOAJ |
| description | In IoT-enabled marine biology, real-time monitoring of marine organisms faces challenges due to blurred images and complex underwater backgrounds, which hinder feature extraction and lead to missed detections. Addressing these issues, the lightweight YOLOv10-AD model introduces AKVanillaNet, a novel backbone optimized for the distinct shapes of marine organisms, improving detection accuracy while minimizing parameters and computational cost. Additionally, the model incorporates the DysnakeConv module within the C2f structure to enhance feature extraction, along with the Powerful-IOU (PIOU) loss function for better data fitting. Testing on URPC dataset shows that YOLOv10-AD achieves an mAP of 85.7%, with a parameter count of 2.45 M, 6.2 GFLOPs, a model size of 5.0 M, and a frame rate of 156 FPS. Compared to the baseline, YOLOv10-AD improves mAP by 5.7% and FPS by 25.8%, while reducing parameters, computational load, and model size by 9.3%, 24.4%, and 9.1%, respectively. This IoT-compatible model enables precise, real-time classification of marine organisms across various lighting conditions, making it a valuable framework for intelligent grading applications in marine biology and advancing IoT-based environmental monitoring. |
| format | Article |
| id | doaj-art-a36c2ca44b394557919b58be758a8cba |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-a36c2ca44b394557919b58be758a8cba2025-02-07T04:47:13ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119379390Lightweight marine biodetection model based on improved YOLOv10Wei Pan0Jiabao Chen1Bangjun Lv2Likun Peng3School of Power Engineering, Naval University of Engineering, Wuhan, 430030, ChinaSchool of Power Engineering, Naval University of Engineering, Wuhan, 430030, ChinaSchool of Power Engineering, Naval University of Engineering, Wuhan, 430030, ChinaCorresponding author.; School of Power Engineering, Naval University of Engineering, Wuhan, 430030, ChinaIn IoT-enabled marine biology, real-time monitoring of marine organisms faces challenges due to blurred images and complex underwater backgrounds, which hinder feature extraction and lead to missed detections. Addressing these issues, the lightweight YOLOv10-AD model introduces AKVanillaNet, a novel backbone optimized for the distinct shapes of marine organisms, improving detection accuracy while minimizing parameters and computational cost. Additionally, the model incorporates the DysnakeConv module within the C2f structure to enhance feature extraction, along with the Powerful-IOU (PIOU) loss function for better data fitting. Testing on URPC dataset shows that YOLOv10-AD achieves an mAP of 85.7%, with a parameter count of 2.45 M, 6.2 GFLOPs, a model size of 5.0 M, and a frame rate of 156 FPS. Compared to the baseline, YOLOv10-AD improves mAP by 5.7% and FPS by 25.8%, while reducing parameters, computational load, and model size by 9.3%, 24.4%, and 9.1%, respectively. This IoT-compatible model enables precise, real-time classification of marine organisms across various lighting conditions, making it a valuable framework for intelligent grading applications in marine biology and advancing IoT-based environmental monitoring.http://www.sciencedirect.com/science/article/pii/S1110016825001048Marine Object DetectionYOLOv10LightweightingGrading detectionObject detection |
| spellingShingle | Wei Pan Jiabao Chen Bangjun Lv Likun Peng Lightweight marine biodetection model based on improved YOLOv10 Alexandria Engineering Journal Marine Object Detection YOLOv10 Lightweighting Grading detection Object detection |
| title | Lightweight marine biodetection model based on improved YOLOv10 |
| title_full | Lightweight marine biodetection model based on improved YOLOv10 |
| title_fullStr | Lightweight marine biodetection model based on improved YOLOv10 |
| title_full_unstemmed | Lightweight marine biodetection model based on improved YOLOv10 |
| title_short | Lightweight marine biodetection model based on improved YOLOv10 |
| title_sort | lightweight marine biodetection model based on improved yolov10 |
| topic | Marine Object Detection YOLOv10 Lightweighting Grading detection Object detection |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825001048 |
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