基于深度学习的生猪饮水行为识别研究  

Pig Drinking Behavior Recognition Based on Deep Learning

作　　者：卞子煜 朱伟兴[1] BIAN Zi-yu;ZHU Wei-xing(School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学电气信息工程学院,江苏镇江212013

出　　处：《软件导刊》2021年第1期72-75,共4页Software Guide

基　　金：国家自然科学基金项目(31172243);江苏高校优势学科建设工程项目(苏政办发[2011]6号)。

摘　　要：计算机视觉技术越来越多地应用于生猪饮水等行为识别中,以判断生猪健康状况。现有的饮水识别方法主要依赖目标轮廓,而传统的阈值分割方式受光照、噪点等因素影响较大,提取的轮廓不够精确。提出一种基于深度学习目标检测算法YOLO(You Only Look Once,YOLO)的生猪行为识别方法,根据生猪位置与饮水区的关系以及是否处于静止状态综合判断其饮水行为。该方法不依赖目标轮廓,且无复杂的手动特征提取过程。在深度学习框架tensorflow上进行群养猪检测、定位以及饮水行为识别。实验证明,该算法比基于轮廓的饮水识别算法精度提高3%,达到94.0%。Computer vision technology has been increasingly used in pig drinking water behavior recognition to judge the health status of pigs.The existing drinking water recognition methods mainly rely on the target contour,while the traditional threshold segmentation method is greatly affected by light,noise and other factors,and the extracted contour is not accurate enough.Therefore,this paper pro⁃poses a pig detection algorithm based on the deep learning target detection algorithm Yolo(you only look once),which identifies the lo⁃cation of the pig,and then comprehensively judges the drinking behavior according to the relationship between the pig location and the drinking water area as well as whether it is still or not.This method does not depend on the contour of the target,and does not need complex manual feature extraction.In this paper,the in-depth learning framework tensorflow is used to detect and locate pigs and iden⁃tify drinking behavior.Experiments show that the accuracy of this algorithm is 3%higher than that of the contour based algorithm,and it reaches 94.0%.

关 键 词：深度学习 目标检测 饮水行为 群养猪 YOLO算法 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]