基于机器视觉的鸡蛋内外品质一体化检测与分级系统  

Integrated Inspecting and Grading System of the Egg Quality Based on Machine Vision

作　　者：梁丹 李平[1] 梁冬泰[1] 陈兴[1] 吴晓成 Liang Dan;Li Ping;Liang Dongtai;Chen Xing;Wu Xiaocheng(Faculty of Mechanical Engineering&Mechanics of Ningbo University,Ningbo 315211,Zhejiang)

机构地区：[1]宁波大学机械与力学学院,浙江宁波315211

出　　处：《中国食品学报》2020年第11期247-254,共8页Journal of Chinese Institute Of Food Science and Technology

基　　金：国家自然科学基金青年科学基金项目(51805280);浙江省自然科学基金项目(LQ18E050005);宁波市自然科学基金项目(2019A610158)。

摘　　要：针对鸡蛋检测分级系统复杂、集成化程度低、多品质因素综合检测的问题,设计出一体化的鸡蛋品质无损检测与分级系统,利用机器视觉算法实现了鸡蛋裂纹、尺寸、新鲜度与品质等级的自动化在线检测与分级。系统主要包括图像采集单元、分级单元、传输单元、图像处理单元和单片机控制单元。基于梯度幅度直方图和类间方差最大法进行自动阈值选取,对一级分级时的裂纹蛋进行剔除;采用外接最小矩形法测量鸡蛋最大横径、最大纵径、蛋行指数;利用鸡蛋透射图颜色信息的变化与哈夫单位值间的关系建立新鲜度BP神经网络,对鸡蛋新鲜度进行分级。试验结果表明,裂纹识别正确率为98.18%,对不同新鲜度等级的鸡蛋品质识别正确率为97.48%。In view of the problems of complexity,low integration and multi-quality factors of egg sorting,an integrated inspecting and grading system of egg qualities was designed,which realized the automated inspecting and classification of the crack,size,freshness and quality level of eggs by using the machine vision algorithm.The designed system adopts pipelined structure which mainly consisted of an image acquisition unit,classification unit,transmission unit,image processing unit and micro-control unit.The image acquisition unit,classification unit,transmission unit were controlled orderly by the image processing unit and microprocessor unit,which were connected by the gigabit net port or the I/O joggle.Aiming at realizing the tiny egg cracks detection,the automatic threshold selection was presented based on the gradient magnitude histogram and maximum variance between clusters.Then,the cracked eggs were detected and identified,and eliminated in the classification unit I.In order to estimate the egg size,the maximum transverse diameter,maximum longitudinal diameter and the egg row index are measured by the minimum enclosed rectangle method.The eggs were sorted into two class of large and small in classification unit II.Through transforming the egg perspective images from RGB to HSI,the relationship between Haff value and the color components is analyzed.A back-propagation neural network is established to classify the freshness.Experimental results show that the accuracy of crack identification is 98.18%,the precision rate of special grade(AA)is 96.67%,the precision rate of grade A(A)is 98.31%,the precision rate of special grade B(B)is 96.67%,and the precision rate of egg quality recognition for different freshness grades is 97.48%generally.The designed system can be applied to online inspection of large-scale poultry and egg farms with high accuracy,simple structure and fast execution speed,as well as automatic inspecting and grading of other agricultural products.

关 键 词：机器视觉 鸡蛋品质 一体化检测 分级系统 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TS253.7[自动化与计算机技术—计算机科学与技术]