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An Improved YOLOv8n Used for Fish Detection in Natural Water Environments
Zehao Zhang1,2,3,4, Yi Qu1,2,3,4, Tan Wang1,2,3,4
1School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China.
Animals : an Open Access Journal From MDPI
|July 27, 2024
Summary
This study introduces BSSFISH-YOLOv8, an accurate computer vision method for fish detection in underwater environments. It enhances detection efficiency and reduces costs in fishery surveys by improving accuracy for small targets.
Area of Science:
- Fisheries Science
- Computer Vision
- Marine Biology
Background:
- Underwater photography presents challenges for accurate fish detection due to environmental complexity.
- Existing computer vision methods have limitations in accuracy for fishery resource surveys.
- Improving detection efficiency and reducing costs are crucial for effective fishery management.
Purpose of the Study:
- To develop an accurate and efficient computer vision method for fish detection in natural underwater environments.
- To address the limitations of current methods in handling underwater image complexity and small targets.
- To enhance the application of computer vision in fishery resource surveys.
Main Methods:
- Proposed BSSFISH-YOLOv8 model incorporating SPD-Conv module to preserve fine-grained information.
- Integrated BiFormer, a dynamic sparse attention technique, into the backbone network for enhanced feature focus and efficiency.
- Introduced a 160 × 160 small target detection layer (STDL) to improve sensitivity to smaller fish.
Main Results:
- BSSFISH-YOLOv8 achieved 88.3% mAP@50 and 58.3% mAP@50:95.
- Performance metrics were 2.0% and 3.3% higher than the YOLOv8n model, respectively.
- Demonstrated improved accuracy and efficiency in detecting fish in complex underwater conditions.
Conclusions:
- BSSFISH-YOLOv8 offers a significant advancement for fish detection in underwater environments.
- The method has the potential to reduce measurement costs and improve efficiency in fishery resource surveys.
- This research contributes to sustainable fishery management through enhanced technological application.
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