| _version_ | 1866901801140224000 |
|---|---|
| author | Kanneboina Ashok Mohaideen A Manivannan.S Radhakrishnan M M. Shailaja |
| author_facet | Kanneboina Ashok Mohaideen A Manivannan.S Radhakrishnan M M. Shailaja |
| contents | <p><span class="fontstyle0">This work aims at studying the species detection of marine animals with deep learning approaches to<br>YOLOv9 and Faster R-CNN algorithms. The main goal is to identify seven categories of species of underwater objects:<br>Fish, Jellyfish, Penguins, Puffins, Sharks ,Starfish, and Stingrays. Here, the study combines the high detection precision<br>of YOLOv9 with 92% with the more robust performance of the Faster R-CNN with 89% to fashion an all-inclusive<br>detection system fit for any underwater environment. For training and testing purposes, a well-selected dataset was used<br>that included a good variety of species to consider every possibility. It can be noted that the use of the proposed methods<br>affords a significant improvement of recognition accuracy and increasing the rate of detection. In many ways, this<br>research suggests that the potential for use of these technologies in such fields as marine biology, and specifically,<br>documenting and conserving marine environments. Thus, using these optimized detection methods, researchers and<br>conservationists can obtain the necessary data on marine diversity and develop the protection of underwater habitats.<br>Finally, this work acknowledges the importance of computer vision in augmenting knowledge of marine life forms.</span> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19588551 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Deep Sea Vision: Enhanced YOLO-Driven Framework for High-Precision Marine Fish Detection Kanneboina Ashok Mohaideen A Manivannan.S Radhakrishnan M M. Shailaja <p><span class="fontstyle0">This work aims at studying the species detection of marine animals with deep learning approaches to<br>YOLOv9 and Faster R-CNN algorithms. The main goal is to identify seven categories of species of underwater objects:<br>Fish, Jellyfish, Penguins, Puffins, Sharks ,Starfish, and Stingrays. Here, the study combines the high detection precision<br>of YOLOv9 with 92% with the more robust performance of the Faster R-CNN with 89% to fashion an all-inclusive<br>detection system fit for any underwater environment. For training and testing purposes, a well-selected dataset was used<br>that included a good variety of species to consider every possibility. It can be noted that the use of the proposed methods<br>affords a significant improvement of recognition accuracy and increasing the rate of detection. In many ways, this<br>research suggests that the potential for use of these technologies in such fields as marine biology, and specifically,<br>documenting and conserving marine environments. Thus, using these optimized detection methods, researchers and<br>conservationists can obtain the necessary data on marine diversity and develop the protection of underwater habitats.<br>Finally, this work acknowledges the importance of computer vision in augmenting knowledge of marine life forms.</span> </p> |
| title | Deep Sea Vision: Enhanced YOLO-Driven Framework for High-Precision Marine Fish Detection |
| url | https://doi.org/10.5281/zenodo.19588551 |