基于YOLOv4的猪耳检测方法研究  

Research on Pig Ear Detection Method Based on YOLOv4

作　　者：左若雨 陈丰 陈蒙 周稳 陈敏权 ZUO Ruoyu;CHEN Feng;CHEN Meng;ZHOU Wen;CHEN Minquan(College of Mechanical Engineering,Anhui Science and Technology University,Fengyang 233100,China)

机构地区：[1]安徽科技学院机械工程学院,安徽凤阳233100

出　　处：《安徽科技学院学报》2022年第4期60-65,共6页Journal of Anhui Science and Technology University

基　　金：安徽省高校协同创新项目(GXXT-2019-003)。

摘　　要：目的:在中小型猪场的复杂环境下,为实现猪只几种常见疾病快速、准确的无人检测与预警,基于YOLO模型,提出一种应用于猪耳部的识别算法模型。方法:以生猪耳部区域为研究对象,应用YOLOv4模型,将生猪耳部区域从猪只个体中提取出来,对其进行详细数据分析及图像增强,图像处理后进行学习训练,实现耳部区域快速检测。结果:应用YOLOv4模型,对生猪耳部区域检测的平均精度值较高,值为97.21%,召回率为92.22%。结论:应用YOLOv4算法可以实现复杂环境下生猪耳部区域快速检测,突出猪只耳部目标,同时对病猪耳部的特征差异进行对比识别,为后续中小型猪场的自动化监测和预警系统提供技术支撑。Objective:In the complex environment of small and medium-sized pig farms,in order to realize the rapid and accurate unmanned detection and early warning of several common diseases of pigs,a recognition algorithm model applied to pig ears is proposed based on YOLO model.Methods:Taking the pig ear area as the research object,the pig ear area was extracted from the pig individual by using YOLOv4 model,and the detailed data analysis and image enhancement were carried out.After image processing,relevant learning and training were carried out,and finally the rapid detection of the ear area was realized.Results:The experiment shows that the average accuracy of detecting pig ear area using YOLOv4 model is high,the value is 97.21%,and the recall rate is 92.22%.Conclusion:The application of YOLOv4 algorithm can realize the rapid detection of pig ear area in complex environment,highlight the pig ear target,and compare and identify the characteristic differences of sick pig ears,so as to provide technical support for the follow-up automatic monitoring and early warning system of small and medium-sized pig farms.

关 键 词：YOLO算法 生猪 猪耳识别 目标检测 图像处理 

分 类 号：TP249[自动化与计算机技术—检测技术与自动化装置]