YOLOv8-MU:一个改进的YOLOv8水下探测器,基于一个大型内核块和一个多分支重组模块
Xing Jiang1, Xiting Zhuang1, Jisheng Chen1
1School of Tropical Agriculture and Forestry (School of Agricultural and Rural, School of Rural Revitalization), Hainan University, Danzhou 571737, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
这项研究介绍了YOLOv8-MU,一种增强的水下视觉检测模型. 它通过使用新的架构组件和专门的损失函数显著提高了海洋目标识别的准确性和稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 海洋生物学 海洋生物学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 准确的水下目标识别对于海洋勘探和监测至关重要.
- 现有的深度学习模型在水下环境中面临挑战,原因是可见度低和复杂的背景等因素.
- 越来越多的人需要更强大,更准确的水下视觉检测技术.
研究的目的:
- 开发一个创新的深度学习架构,YOLOv8-MU,用于增强水下视觉检测.
- 提高水下目标识别模型的准确性,稳定性和概括能力.
- 解决水下生物检测的特定挑战,如定位精度和边界清晰度.
主要方法:
- 拟议的YOLOv8-MU架构集成了大型内核块 (LarK块) 以实现优化的骨干.
- 它结合了C2fSTR (带有C2f模块的Swin变压器) 和SPPFCSPC_EMA (带注意的SPPFCSPC) 进行了改进的特征提取.
- 来自DAMO-YOLO的融合块增强了多尺度特征提取,MPDIoU损失优化了本地化准确性.
主要成果:
- 在URPC2019数据集上,YOLOv8-MU实现了78.4%的mAP@0.5,比YOLOv8.0有4.0%的改善.
- 该模型在URPC2020上达到80.9%,在水族馆数据集上达到75.5%,超过YOLOv5和YOLOv8n.
- 在改进的URPC2019数据集上,YOLOv8-MU表现出最先进的性能,mAP@0.5为88.1%.
结论:
- YOLOv8-MU显著提高了水下视觉检测的准确性和稳定性.
- 该模型在各种水下数据集中表现出强大的概括能力.
- 拟议的建筑改进和损失函数为海洋勘探和监测应用提供了卓越的解决方案.
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