An Improved YOLOv8 and OC-SORT Framework for Fish Counting
Accurate fish population estimation is crucial for fisheries management, ecological monitoring, and aquaculture optimization. Traditional manual counting methods are labor-intensive and error-prone, while existing automated approaches struggle with occlusions, small-object detection, and identity sw...
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MDPI AG
2025-05-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/6/1016 |
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| author | Yan Li Zhenpeng Wu Ying Yu Chichi Liu |
| author_facet | Yan Li Zhenpeng Wu Ying Yu Chichi Liu |
| author_sort | Yan Li |
| collection | DOAJ |
| description | Accurate fish population estimation is crucial for fisheries management, ecological monitoring, and aquaculture optimization. Traditional manual counting methods are labor-intensive and error-prone, while existing automated approaches struggle with occlusions, small-object detection, and identity switches. To address these challenges, this paper proposes an improved fish counting framework integrating YOLOv8-DT for detection and Byte-OCSORT for tracking. YOLOv8-DT incorporates the Deformable Large Kernel Attention Cross Stage Partial (DLKA CSP) module for adaptive receptive field adjustment and the Triple Detail Feature Infusion (TDFI) module for enhanced multi-scale feature fusion, improving small-object detection and occlusion robustness. Byte-OCSORT extends OC-SORT by integrating ByteTrack’s two-stage matching and a Class-Aware Cost Matrix (CCM), reducing ID switches and improving multi-species tracking stability. Experimental results on real-world underwater datasets demonstrate that YOLOv8-DT achieves a mAP<sub>50</sub> of 0.971 and mAP<sub>50:95</sub> of 0.742, while Byte-OCSORT reaches a MOTA of 72.3 and IDF1 of 69.4, significantly outperforming existing methods, confirming the effectiveness of the proposed framework for robust and accurate fish counting in complex aquatic environments. |
| format | Article |
| id | doaj-art-821f73385f5a42ca93cc567cdc8d1ebd |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-821f73385f5a42ca93cc567cdc8d1ebd2025-08-20T03:27:19ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-05-01136101610.3390/jmse13061016An Improved YOLOv8 and OC-SORT Framework for Fish CountingYan Li0Zhenpeng Wu1Ying Yu2Chichi Liu3State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen 361105, ChinaAccurate fish population estimation is crucial for fisheries management, ecological monitoring, and aquaculture optimization. Traditional manual counting methods are labor-intensive and error-prone, while existing automated approaches struggle with occlusions, small-object detection, and identity switches. To address these challenges, this paper proposes an improved fish counting framework integrating YOLOv8-DT for detection and Byte-OCSORT for tracking. YOLOv8-DT incorporates the Deformable Large Kernel Attention Cross Stage Partial (DLKA CSP) module for adaptive receptive field adjustment and the Triple Detail Feature Infusion (TDFI) module for enhanced multi-scale feature fusion, improving small-object detection and occlusion robustness. Byte-OCSORT extends OC-SORT by integrating ByteTrack’s two-stage matching and a Class-Aware Cost Matrix (CCM), reducing ID switches and improving multi-species tracking stability. Experimental results on real-world underwater datasets demonstrate that YOLOv8-DT achieves a mAP<sub>50</sub> of 0.971 and mAP<sub>50:95</sub> of 0.742, while Byte-OCSORT reaches a MOTA of 72.3 and IDF1 of 69.4, significantly outperforming existing methods, confirming the effectiveness of the proposed framework for robust and accurate fish counting in complex aquatic environments.https://www.mdpi.com/2077-1312/13/6/1016fish countingobject detectionmulti-object trackingYOLOv8-DTByte-OCSORTunderwater fish monitoring |
| spellingShingle | Yan Li Zhenpeng Wu Ying Yu Chichi Liu An Improved YOLOv8 and OC-SORT Framework for Fish Counting Journal of Marine Science and Engineering fish counting object detection multi-object tracking YOLOv8-DT Byte-OCSORT underwater fish monitoring |
| title | An Improved YOLOv8 and OC-SORT Framework for Fish Counting |
| title_full | An Improved YOLOv8 and OC-SORT Framework for Fish Counting |
| title_fullStr | An Improved YOLOv8 and OC-SORT Framework for Fish Counting |
| title_full_unstemmed | An Improved YOLOv8 and OC-SORT Framework for Fish Counting |
| title_short | An Improved YOLOv8 and OC-SORT Framework for Fish Counting |
| title_sort | improved yolov8 and oc sort framework for fish counting |
| topic | fish counting object detection multi-object tracking YOLOv8-DT Byte-OCSORT underwater fish monitoring |
| url | https://www.mdpi.com/2077-1312/13/6/1016 |
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