摘要
目的:解决现有食品生产企业在核桃缺陷检测中存在的准确性低和效率差等问题。方法:提出一种结合改进极限学习机和计算机视觉的核桃缺陷快速无损检测方法。通过计算机视觉采集核桃大部分表面图像信息,通过高斯滤波对图像进行预处理,通过迭代和保留信息变量法对颜色和纹理特征进行优化,最后,通过改进蝴蝶算法对极限学习机参数(随机权重和偏差)进行优化,实现核桃缺陷快速无损检测,并对所提缺陷检测方法的性能进行验证。结果:试验方法可以实现核桃多种缺陷的有效判别。与常规方法相比,试验方法在核桃缺陷检测中具有更优的检测准确率和效率,检测准确率>98.00%,平均检测时间<9.00 ms。结论:将智能算法和机器视觉技术相结合可以实现核桃缺陷的快速无损检测。
Objective:To address the issues of low accuracy and poor efficiency in walnut defect detection among existing food production enterprises.Methods:Proposed a fast non-destructive detection method for walnut defects that combined improved extreme learning machines and computer vision.Collected most of the surface image information of walnuts through computer vision,preprocess the image through Gaussian filtering,optimize color and texture features through iterative and information preserving variable methods,finally,by improving the butterfly algorithm to optimize the parameters of the Extreme Learning Machine(random weights and deviations),fast non-destructive detection of walnut defects could be achieved,and verify the performance of the proposed defect detection method.Results:The experimental method could effectively discriminate various defects in walnuts.Compared with conventional methods,the experimental method had superior detection accuracy and efficiency in walnut defect detection,with a detection accuracy rate>98.00% and an average detection time<9.00 ms.Conclusion:Combining intelligent algorithms with machine vision technology can achieve rapid non-destructive detection of walnut defects.
作者
徐杰
刘畅
XU Jie;LIU Chang(Jiangxi V&T College of Communications,Nanchang,Jiangxi 330013,China;Nanchang Hangkong University,Nanchang,Jiangxi 330063,China)
出处
《食品与机械》
CSCD
北大核心
2024年第5期122-127,共6页
Food and Machinery
基金
国家自然科学基金项目(编号:62262043)。
关键词
食品生产
核桃缺陷
计算机视觉
极限学习机
蝴蝶优化算法
快速无损检测
food production
walnut defects
computer vision
extreme learning machine
butterfly optimization algorithm
rapid non-destructive testing
