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YOLOv8-TF:转换器增强的YOLOv8用于水下鱼类物种识别与类不平衡处理
Chiranjibi Shah1, M M Nabi2, Simegnew Yihunie Alaba3
1Northern Gulf Institute, Mississippi State University, Starkville, MS 39759, USA.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
一个新的变压器增强的YOLOv8模型改善了水下鱼类物种的识别. 这种方法提高了对象检测的准确性,以更好地评估库存和保护工作.
科学领域:
- 海洋生物学 海洋生物学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 准确的鱼类物种识别对于海洋生态系统的管理和保护至关重要.
- 水下视频调查面临的挑战包括可见度差和鱼类的多样性,阻碍了传统的检测方法.
研究的目的:
- 开发一个先进的物体检测模型,用于强大的水下鱼类物种识别.
- 增强YOLOv8算法,在具有挑战性的水生环境中提高准确性.
主要方法:
- 提出了一种改进了变压器的YOLOv8 (YOLOv8-TF) 模型,其中包含了一个变压器块和一个类意识损失函数.
- 调整了深度尺度,并使用了类意识损失函数来解决数据不平衡,并改善稀有物种的识别.
- 在SEAMAPD21数据集和Pascal VOC和MS COCO等公共基准上评估了该模型.
主要成果:
- 在SEAMAPD21数据集上,YOLOv8-TF模型实现了高性能,mAP0.5达到87.9%,mAP0.5-0.95达到61.2%.
- 与现有的最先进的YOLO模型相比,在水下鱼类物种识别方面取得了卓越的检测结果.
- 在公开可用的Pascal VOC和MS COCO数据集上表现优于当前的方法.
结论:
- YOLOv8-TF模型在水下鱼类物种识别方面取得了重大进展.
- 这种增强的模型为生态监测,库存评估和海洋生物保护提供了更准确的工具.
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