李小昱,陶海龙,高海龙,李 鹏,黄 涛,孙金风.马铃薯缺陷透射和反射机器视觉检测方法分析[J].农业机械学报,2014,45(5):191-196.
Li Xiaoyu,Tao Hailong,Gao Hailong,Li Peng,Huang Tao,Sun Jinfeng.Comparison of Transmission and Reflection Imaging Technologies to Detect Potato Defects Based on Machine Vision Technology[J].Transactions of the Chinese Society for Agricultural Machinery,2014,45(5):191-196.
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马铃薯缺陷透射和反射机器视觉检测方法分析   [下载全文]
Comparison of Transmission and Reflection Imaging Technologies to Detect Potato Defects Based on Machine Vision Technology   [Download Pdf][in English]
投稿时间:2013-06-19  
DOI:10.6041/j.issn.1000-1298.2014.05.030
中文关键词:  马铃薯 缺陷检测 透射图像 反射图像 机器视觉
基金项目:国家自然科学基金资助项目(61275156)、湖北省自然科学基金资助项目(2011CDA033)和中央高校基本科研业务费专项基金资助项目(0900205116)
作者单位
李小昱 华中农业大学 
陶海龙 华中农业大学 
高海龙 华中农业大学 
李 鹏 华中农业大学 
黄 涛 华中农业大学 
孙金风 湖北工业大学 
中文摘要:针对反射机器视觉技术若同时检测马铃薯内外部缺陷存在检测精度不高的问题,提出一种基于透射机器视觉技术的马铃薯内外部缺陷无损检测方法。通过对获取的马铃薯透射和反射图像预处理方法的比较研究,确定上山法结合区域生长法为马铃薯透射和反射图像特征的最优分割方法;采用偏最小二乘-支持向量机分别建立了透射和反射图像的马铃薯缺陷识别模型并进行了比较。在对马铃薯内部缺陷进行检测时,透射和反射图像所建模型的判别正确率分别为96.30%、59.26%;在对马铃薯外部缺陷进行检测时,透射和反射图像所建模型的判别正确率分别为94.20%、89.86%;在对马铃薯内外部缺陷进行同时检测时,透射和反射图像所建模型的判别正确率分别为95.83%、81.25%。研究结果表明,无论是对马铃薯内部或外部缺陷单独进行检测,还是对内外部缺陷同时进行检测,透射方法均比反射方法精度更高。
Li Xiaoyu  Tao Hailong  Gao Hailong  Li Peng  Huang Tao  Sun Jinfeng
Huazhong Agricultural University;Huazhong Agricultural University;Huazhong Agricultural University;Huazhong Agricultural University;Huazhong Agricultural University;Hubei University of Technology
Key Words:Potato Defect detection Transmission image Reflection image Machine vision
Abstract:With the aim to solve the accurate rate short of reflection imaging technology to simultaneously detecting internal and external defects of potatoes, a nondestructive test technology based on transmission imaging and machine vision technology was proposed. It is concluded that the combination of hill climbing method and region growing method is the optimal image segmentation method for transmission and reflection images of potato by studying image preprocessing methods. Partial least squares-support vector machine (PLS-SVM) method was employed to establish the potato defects recognition model for transmission and reflection images of potato. In the potato internal defects detection, the classifying correct rates of the transmission and the reflection imaging technology are 96.30% and 59.26% respectively; in the potato external defects detection, the classifying correct rates are 94.20% and 89.86% respectively; in the simultaneous potato internal and external defects detection, the classifying correct rates are 95.83% and 81.25% respectively. The research results show that the transmission method is better than the reflection method in detecting potato internal and external defects alone, or in detecting the internal and external defects simultaneously.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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