Detecting and quantifying deep sea benthic life using advanced object detection
We present a new dataset combined with the DeepSee model, which utilizes the YOLOv8 architecture, designed to rapidly and accurately detect benthic lifeforms in deep-sea environments of the North Atlantic. The dataset consists of 2,825 carefully curated images, encompassing 20,076 instances across 1...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2024.1470424/full |
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| author | Karthik H. Iyer Camilla M. Marnor Daniel W. Schmid Ebbe H. Hartz |
| author_facet | Karthik H. Iyer Camilla M. Marnor Daniel W. Schmid Ebbe H. Hartz |
| author_sort | Karthik H. Iyer |
| collection | DOAJ |
| description | We present a new dataset combined with the DeepSee model, which utilizes the YOLOv8 architecture, designed to rapidly and accurately detect benthic lifeforms in deep-sea environments of the North Atlantic. The dataset consists of 2,825 carefully curated images, encompassing 20,076 instances across 15 object-detection classes based on morphospecies from the phyla Arthropoda, Chordata, Cnidaria, Echinodermata, and Porifera. When benchmarked against a published dataset from the same region, DeepSee achieves high performance metrics, including an impressive mean Average Precision (mAP) score of 0.84, and produces very few false positives, ensuring reliable detection. The model processes images at 28–50 frames per second (fps) for images sized at 1280 pixels, significantly increasing processing speed and reducing annotation workloads by over 1000 times when compared to manual annotation. While the model is not intended to replace the expertise of experienced biologists, it provides a valuable tool for accelerating data analysis and increasing efficiency. As additional data becomes available, augmenting the dataset and retraining the model will enable further improvements in detection capabilities. The dataset and model are designed for extensibility, allowing for the inclusion of other benthic lifeforms from the North Atlantic and beyond. This capability supports the creation of high-resolution maps of benthic life on the largely unexplored ocean floor of the Norwegian Continental Shelf (NCS) and other regions. This will facilitate informed decision-making in marine resource exploration, including mining operations, bottom trawling, and deep-sea pipeline laying, while also contributing to marine conservation and the sustainable management of deep-sea ecosystems. |
| format | Article |
| id | doaj-art-515bc65ad460417294eb6ca5a52fad00 |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-515bc65ad460417294eb6ca5a52fad002025-01-13T05:10:15ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-01-011110.3389/fmars.2024.14704241470424Detecting and quantifying deep sea benthic life using advanced object detectionKarthik H. Iyer0Camilla M. Marnor1Daniel W. Schmid2Ebbe H. Hartz3Bergverk AS, Sandefjord, NorwayBergverk AS, Sandefjord, NorwayBergverk AS, Sandefjord, NorwayAkerBP ASA, Lysaker, NorwayWe present a new dataset combined with the DeepSee model, which utilizes the YOLOv8 architecture, designed to rapidly and accurately detect benthic lifeforms in deep-sea environments of the North Atlantic. The dataset consists of 2,825 carefully curated images, encompassing 20,076 instances across 15 object-detection classes based on morphospecies from the phyla Arthropoda, Chordata, Cnidaria, Echinodermata, and Porifera. When benchmarked against a published dataset from the same region, DeepSee achieves high performance metrics, including an impressive mean Average Precision (mAP) score of 0.84, and produces very few false positives, ensuring reliable detection. The model processes images at 28–50 frames per second (fps) for images sized at 1280 pixels, significantly increasing processing speed and reducing annotation workloads by over 1000 times when compared to manual annotation. While the model is not intended to replace the expertise of experienced biologists, it provides a valuable tool for accelerating data analysis and increasing efficiency. As additional data becomes available, augmenting the dataset and retraining the model will enable further improvements in detection capabilities. The dataset and model are designed for extensibility, allowing for the inclusion of other benthic lifeforms from the North Atlantic and beyond. This capability supports the creation of high-resolution maps of benthic life on the largely unexplored ocean floor of the Norwegian Continental Shelf (NCS) and other regions. This will facilitate informed decision-making in marine resource exploration, including mining operations, bottom trawling, and deep-sea pipeline laying, while also contributing to marine conservation and the sustainable management of deep-sea ecosystems.https://www.frontiersin.org/articles/10.3389/fmars.2024.1470424/fulldeep sea benthic lifeobject detectionmachine learningmarine resourcesMPA (marine protected area) |
| spellingShingle | Karthik H. Iyer Camilla M. Marnor Daniel W. Schmid Ebbe H. Hartz Detecting and quantifying deep sea benthic life using advanced object detection Frontiers in Marine Science deep sea benthic life object detection machine learning marine resources MPA (marine protected area) |
| title | Detecting and quantifying deep sea benthic life using advanced object detection |
| title_full | Detecting and quantifying deep sea benthic life using advanced object detection |
| title_fullStr | Detecting and quantifying deep sea benthic life using advanced object detection |
| title_full_unstemmed | Detecting and quantifying deep sea benthic life using advanced object detection |
| title_short | Detecting and quantifying deep sea benthic life using advanced object detection |
| title_sort | detecting and quantifying deep sea benthic life using advanced object detection |
| topic | deep sea benthic life object detection machine learning marine resources MPA (marine protected area) |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2024.1470424/full |
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