| 基于机器视觉的樱桃缺陷检测与识别 |
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| 引用本文： | 裴悦琨,连明月,姜艳超,叶家敏,韩心新,谷宇. 基于机器视觉的樱桃缺陷检测与识别[J]. 食品与机械, 2019, 35(12): 137-140 |
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| 作者姓名： | 裴悦琨 连明月 姜艳超 叶家敏 韩心新 谷宇 |
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| 作者单位： | 大连大学辽宁省北斗高精度位置服务技术工程实验室，辽宁 大连 116622；大连大学大连市环境感知与智能控制重点实验室，辽宁 大连 116622 |
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| 基金项目： | 国家自然科学基金项目（编号：61601076）；国防重点实验室开放基金项目（编号：614240101060217）；辽宁省博士启动基金项目（编号：20170520159） |
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| 摘 要： | 以机器视觉技术为基础,利用卷积神经网络对樱桃缺陷进行检测与识别,并进行验证。结果表明,正常果樱桃识别准确率为99.25%,缺陷果樱桃识别准确率为97.99%,识别速度为25个/s;通过与其他方法进行对比,试验方法能够准确检测并识别多种缺陷类型。
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| 关 键 词： | 缺陷检测 樱桃分级 机器视觉 卷积神经网络 |
| 收稿时间： | 2019-08-06 |
Cherry defect detection and recognition based on machine vision |
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| PEI Yue kun,LIAN Ming yue,JIANG Yan chao,YE Jia min,HAN Xin xin,GU Yu. Cherry defect detection and recognition based on machine vision[J]. Food and Machinery, 2019, 35(12): 137-140 |
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| Authors: | PEI Yue kun LIAN Ming yue JIANG Yan chao YE Jia min HAN Xin xin GU Yu |
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| Affiliation: | Beidou High Precision Positioning Service Technology Engineering Laboratory of Liaoning Province, Dalian University, Dalian, Liaoning 116622, China; Environment Sensing and Intelligent Control Key Laboratory of Dalian, Dalian University, Dalian, Liaoning 116622, China |
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| Abstract: | Based on the machine vision technology, convolutional neural network (CNN) was used to detect and recognize, and verified the cherry defects. The results showed that the recognition accuracy of intact cherry was 99.25%, with the average recognition accuracy of defective cherry of 97.99%, and the recognition speed was 25 per second. Compared with other research methods, this method could accurately detect and identify various types of defects. |
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| Keywords: | defect detection cherry grading machine vision convolutional neural network (CNN) |
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