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基于YOLOv5的实时鱼类检测和细分的多任务模型.
QinLi Liu1, Xinyao Gong1, Jiao Li1
1College of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了一种基于YOLOv5的模型,用于实时鱼类检测和细分,增强智能鱼类养殖. 新方法在细分鱼类图像方面实现了高精度,这对于高效的水产养殖监测至关重要.
科学领域:
- 水产养殖技术 水产养殖技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 准确的养鱼和实时监测对于开发智能水产养殖系统至关重要.
- 现有的实例细分网络,如Mask R-CNN,在实时监控鱼类检测和细分的有效性方面遇到了困难.
- 提高鱼类图像细分精度对于提高鱼类养殖行业的精度和智能是至关重要的.
研究的目的:
- 为实时鱼类检测和细分开发一个准确和高效的算法.
- 通过先进的图像分析,提高养殖鱼类的智能和精度.
- 解决现有实时鱼类监测方法的局限性.
主要方法:
- 利用YOLOv5作为对象检测的骨干网络.
- 与YOLOv5架构集成了一个语义细分头.
- 开发了一种混合模型,将对象检测和语义细分结合起来,用于鱼类图像分析.
主要成果:
- 在金色十字鱼数据集上实现了95.4%的物体检测精度和98.5%的语义细分精度.
- 在RTX3060显卡上展示了116.6 FPS的处理速度.
- 在PASCAL VOC 2007数据集上验证了性能,检测精度为73.8%,细分精度为84.3%,FPS为120.
结论:
- 拟议的基于YOLOv5的算法显著提高了实时鱼类检测和细分精度.
- 开发的模型是有效的智能养鱼应用程序,提供高精度和速度.
- 这种方法代表了水产养殖自动化监测系统的重大进步.
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