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基于循环一致迁移神经网络的子子检测的头部特征增强算法

Zhenwei Yu¹, Liqing Wan¹, Khurram Yousaf²

¹College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an 271018, China; Shandong Provincial Engineering Laboratory of Agricultural Equipment Intelligence, Shandong Provincial Key Laboratory of Horticultural Machineries and Equipment, Tai'an 271018, China.

Poultry science

|April 11, 2024

概括

这项研究引入了一种新的算法,以消除家禽养殖场图像中的子门干扰,显著提高了子和眼睛的检测精度. 这一进步有助于更好地监测子的健康状况.

科学领域:

农业工程农业工程
计算机视觉计算机视觉
动物科学动物科学

背景情况:

工业养养殖依赖于封闭的多层住宅.
准确检测健康指标,如和眼睛,对于农场管理至关重要.
图像检测的准确性往往受到围入口的损害,降低了精度.

研究的目的:

提出一种基于深度学习的算法,用于删除家禽图像中的子门障碍物.
为了提高检测子子和眼睛健康评估的准确性.
用各种YOLOv8模型验证拟议的算法的有效性.

主要方法:

使用循环一致迁移神经网络 (CCMNN) 开发一个子移除算法.
应用CCMNN网络用于自动删除和恢复图像信息.
使用结构相似度指数测量 (SSIM) 和峰值信号噪声比 (PSNR) 评估图像质量.
在CCMNN处理之前和之后对目标检测算法 (YOLOv8变体) 性能进行比较分析.

主要成果:

在图像恢复方面,CCMNN算法实现了91.14%的SSIM和25.34dB的PSNR.
在CCMNN处理后,在不同的YOLOv8模型中,检测精度提高了10.212.8%.
在CCMNN处理后,不同YOLOv8模型的眼睛检测精度提高了2.410.2%.

结论:

拟议的CCMNN算法有效地消除了子门干扰,产生了更完整的图像.
和眼睛检测精度的显著提高有助于改善养的健康监测.
这项研究为开发用于家禽生产和农场状况评估的先进检测设备提供了基础.

关键词:

子门子门子门在子里养的肉循环一致迁移神经网络的神经网络对象检测检测对象检测对象检测

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