A novel approach combining YOLO and DeepSORT for detecting and counting live fish in natural environments through video
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| Wydane w: | PLoS One vol. 20, no. 6 (Jun 2025), p. e0323547 |
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| Kolejni autorzy: | , , |
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Public Library of Science
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| Dostęp online: | Citation/Abstract Full Text Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3218003143 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0323547 |2 doi | |
| 035 | |a 3218003143 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Nguyen Minh Khiem | |
| 245 | 1 | |a A novel approach combining YOLO and DeepSORT for detecting and counting live fish in natural environments through video | |
| 260 | |b Public Library of Science |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Applying Artificial Intelligence (AI) to the monitoring of live fish in natural environments represents a promising approach to the sustainable management of aquatic resources. Detecting and counting fish in water through video analysis is crucial for fish population statistics. This study employs AI algorithms, specifically YOLOv10 (You Only Look Once version 10) for identifying the presence fish in video frames, combined with the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm to count the number of fish individual moving across the frames. A total of 9,002 frames were extracted from 13 videos recorded in five different environments: areas with submerged tree roots, shallow marine regions, coral reefs, bleached coral reefs and seagrass meadows. To train the recognition model, the dataset was divided into training, validation and testing sets in 8:1:1 ratio. The results demonstrated that the model achieved an accuracy of 89.5%, with processing times of 6.2ms for preprocessing, 387.0ms for inference and 0.9ms for postprocessing per image. The combination of YOLO and DeepSORT enhances the accuracy of tracking objects in aquatic environments, showing great potential for the monitoring of fishery resources. | |
| 653 | |a Behavior | ||
| 653 | |a Accuracy | ||
| 653 | |a Fisheries | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Datasets | ||
| 653 | |a Fish | ||
| 653 | |a Fish populations | ||
| 653 | |a Sustainability management | ||
| 653 | |a Algorithms | ||
| 653 | |a Coral reefs | ||
| 653 | |a Population statistics | ||
| 653 | |a Fishery resources | ||
| 653 | |a Aquatic environment | ||
| 653 | |a Automation | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Monitoring | ||
| 653 | |a Tracking | ||
| 653 | |a Research & development--R&D | ||
| 653 | |a Frames (data processing) | ||
| 653 | |a Computer vision | ||
| 653 | |a Coral bleaching | ||
| 653 | |a Neural networks | ||
| 653 | |a Natural environment | ||
| 653 | |a Fishing | ||
| 653 | |a Object recognition | ||
| 653 | |a Commercial fishing | ||
| 653 | |a Population studies | ||
| 653 | |a Fisheries management | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Tran Van Thanh | |
| 700 | 1 | |a Nguyen, Hung Dung | |
| 700 | 1 | |a Takahashi, Yuki | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 6 (Jun 2025), p. e0323547 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3218003143/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3218003143/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3218003143/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |