SD-YOLOv8:基于改进的YOLOv8的一个准确的Seriola dumerili检测模型
Mingxin Liu1,2, Ruixin Li3, Mingxin Hou2,4
1School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
本研究介绍了SD-YOLOv8,这是一个先进的模型,用于在具有挑战性的水下环境中准确识别Seriola dumerili (大珀). 该模型显著提高了鱼类检测的准确性,有利于水产养殖和研究.
科学领域:
- 计算机视觉 计算机视觉
- 水产养殖技术 水产养殖技术
- 海洋生物学 海洋生物学
背景情况:
- 精确识别Seriola dumerili (大珀) 对于水产养殖和行为研究至关重要.
- 水下环境带来了诸如可变照明和鱼群培养等挑战,阻碍了精确的物种识别.
研究的目的:
- 开发一个智能识别模型,SD-YOLOv8,用于增强检测Seriola dumerili.
- 在复杂的水生环境中,提高近距离和远距离鱼类实例的识别精度.
主要方法:
- 提出了一个智能识别模型,SD-YOLOv8,基于YOLOv8网络架构.
- 包含一个小型物体检测层和头部,以增强检测较小或遥远的目标.
- 利用可变形卷积网络v2 (DCNv2),瓶注意模块 (BAM) 和重新设计的空间金字塔聚合融合 (SPPF) 改进了特征提取和融合.
- 为了精确定位,使用了内部-MPDIoU界限框回归.
主要成果:
- SD-YOLOv8模型在检测准确度和平均精度方面取得了显著的改进.
- 精度从89.2%增加到93.2%,平均精度从92.2%增加到95.7%.
- 该模型有效地提高了在具有挑战性的水下条件下识别能力.
结论:
- 开发的SD-YOLOv8模型提供了一种可靠和准确的方法来识别Seriola dumerili.
- 这一进步为Seriola dumerili水产养殖和行为研究提供了关键的技术支持.
- 该模型的增强检测精度有助于更广泛的鱼类识别技术领域.
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