基于机器视觉的小麦并肩杂与不完善粒动态实时检测研究
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摘要
我国是世界上最大的小麦生产国和消费国,对小麦质量进行等级划分的重要意义不言而喻。目前依据国标对小麦并肩杂和不完善粒的检测仍采用人工方法,而基于机器视觉技术的动态实时检测能有效保障其结果的客观性,并提高检测精度和速度。本论文综合应用光学、高光谱、数字图像处理及模式识别技术,研究了基于机器视觉的小麦并肩杂与不完善粒动态实时检测方法,并结合机械、电子、计算机等技术进行样机研制。
主要研究内容和结论如下：
1.小麦高光谱实验与RGB图像采集系统构建。通过对高光谱数据做主成分分析,得到最有效主成分,由最有效主成分中各波段的贡献率确定最佳波段。针对小麦外观特征的有效提取,经研究630nnm波段的贡献率最大。根据该波段确定相机的波段光谱响应参数,构建了RGB图像采集系统。
2.小麦动态图像预处理与特征提取。通过不同颜色背景下的小麦图像直方图和对比度分析,确定黑色不反光背景为小麦并肩杂与不完善粒检测的最佳背景。对采集到的小麦动态图像进行多余背景去除、同态滤波等图像预处理,并通过阈值分割和形态学处理进行形态特征描述、通过颜色空间变换进行颜色特征描述、通过二维小波变换进行纹理特征描述,分别提取了7个形态特征、27个颜色特征和144个纹理特征。
3.小麦并肩杂和不完善粒识别算法研究。对1169个正常小麦、897个并肩杂、710个黑胚粒小麦和627个破损小麦样本所提取的特征数据,分别采用遗传算法与支持向量机(GA-SVM)、主成分分析与支持向量机(PCA-SVM)、偏最小二乘判别分析(PLS-DA)、偏最小二乘与支持向量机(PLS-SVM)、主成分分析与人工神经网络(PCA-ANN)和线性判别分析(LDA)算法进行模式识别比较分析,结果表明GA-SVM对小麦并肩杂的识别率最高可达99.34%,PCA-SVM对不完善粒中的黑胚粒、破损粒和正常小麦的识别率分别为97.2%、98.4%和97.9%。
4.小麦并肩杂与不完善粒动态实时检测装置研制。采用旋转输送方式进行小麦单粒化喂入和下料分级,通过集成机械部分、电路模块和计算机视觉系统,将GA-SVM算法用于动态实时检测,实现了软件对相机设置、图像采集、图像处理、特征提取、模式识别、数据通讯和机构运动的实时控制,研制了小麦并肩杂与不完善粒动态实时检测装置。
China is the largest producer and consumer of wheat in the world, and the inspection of wheat quality is very important for the grain industry. Up to now, the inspection of Kernel-like Impurity and Unsound Kernel in wheat is rely upon manual method still, which is neither objective nor efficient. Machine vision provides a rapid and objective means for evaluating the appearance quality of wheat. The research integrates knowledge of optics, hyperspectral, image processing and pattern recognition to analyze the real-time detection of the Kernel-like Impurity and Unsound Kernels in wheat, and a prototype was explored utilizing with technologies of mechanism, electronic and computer. The main contents are as follows:
1. Hyperspectral experiments and image acquisition system. Hyperspectral images were used to determine the optimal band according to each band's contribution to the most effective principal component. The study has found that the contribution of 630 nm band is the most useful band for morphological feature, which is right close to the wavelength of the red light used in this thesis.
2. Real-time wheat image preprocessing and feature extraction. Through the image histograms and the contrast analysis of wheat under different color backgrounds, the black and opaque background turns out to be the best one for wheat identification. After pretreatment and some image process steps, including unnecessary background cutting, homomorphic filter, image threshold segmentation, morphological image processing, color space conversion and wavelet transform,7 morphological features,27 color features and 144 texture features were extracted.
3. Comparison of different pattern recognition methods of Kernel-like impurity and Unsound Kernel in wheat. The samples include 1169 Sound Kernels,897 Kernel-like Impurities,710 Black Germ kernels, and 627 Damaged Kernels. There are several analysis algorithms used for data processing, such as genetic algorithm and the support vector machine (GA-SVM), principal component analysis and support vector machine (PCA-SVM), partial least squares discriminant analysis (PLS-DA), partial least squares and support vector machine(PLS-SVM), principal component analysis and artificial neural network (PCA-ANN), linear discriminant analysis(LDA). According to the research, combining genetic algorithm and the support vector machine (GA-SVM) is the best method, the accuracy of which was 99.34%; PCA-SVM gets the best accuracy:97.2%,98.4% and 97.9%for Black Germ Kernels, Broken Kernels and Sound Kernels.
4. Prototype development for real-time detection of Kernel-like impurity and Unsound Kernel in wheat. The mechanical part mainly includes the main stents, the single graining device, the transmission device and the material feeding device. The circuits section of the machine (lower computer) consists of core chip of ATmega128, communication circuit chip and some peripheral circuits. According to the image processing and pattern recognition method determined in experiment, the code is realized with VC++ software. The Personal computer (upper computer) can control the camera setting, image acquisition, image processing, feature extraction, pattern recognication and communication directly. The hardware and software are collaboratively debugged, and finally all software used is packaged into a setup.exe bag. Users double-click setup.exe to complete software installation and then it will be ready to work.
主要研究内容和结论如下：
1.小麦高光谱实验与RGB图像采集系统构建。通过对高光谱数据做主成分分析,得到最有效主成分,由最有效主成分中各波段的贡献率确定最佳波段。针对小麦外观特征的有效提取,经研究630nnm波段的贡献率最大。根据该波段确定相机的波段光谱响应参数,构建了RGB图像采集系统。
2.小麦动态图像预处理与特征提取。通过不同颜色背景下的小麦图像直方图和对比度分析,确定黑色不反光背景为小麦并肩杂与不完善粒检测的最佳背景。对采集到的小麦动态图像进行多余背景去除、同态滤波等图像预处理,并通过阈值分割和形态学处理进行形态特征描述、通过颜色空间变换进行颜色特征描述、通过二维小波变换进行纹理特征描述,分别提取了7个形态特征、27个颜色特征和144个纹理特征。
3.小麦并肩杂和不完善粒识别算法研究。对1169个正常小麦、897个并肩杂、710个黑胚粒小麦和627个破损小麦样本所提取的特征数据,分别采用遗传算法与支持向量机(GA-SVM)、主成分分析与支持向量机(PCA-SVM)、偏最小二乘判别分析(PLS-DA)、偏最小二乘与支持向量机(PLS-SVM)、主成分分析与人工神经网络(PCA-ANN)和线性判别分析(LDA)算法进行模式识别比较分析,结果表明GA-SVM对小麦并肩杂的识别率最高可达99.34%,PCA-SVM对不完善粒中的黑胚粒、破损粒和正常小麦的识别率分别为97.2%、98.4%和97.9%。
4.小麦并肩杂与不完善粒动态实时检测装置研制。采用旋转输送方式进行小麦单粒化喂入和下料分级,通过集成机械部分、电路模块和计算机视觉系统,将GA-SVM算法用于动态实时检测,实现了软件对相机设置、图像采集、图像处理、特征提取、模式识别、数据通讯和机构运动的实时控制,研制了小麦并肩杂与不完善粒动态实时检测装置。
China is the largest producer and consumer of wheat in the world, and the inspection of wheat quality is very important for the grain industry. Up to now, the inspection of Kernel-like Impurity and Unsound Kernel in wheat is rely upon manual method still, which is neither objective nor efficient. Machine vision provides a rapid and objective means for evaluating the appearance quality of wheat. The research integrates knowledge of optics, hyperspectral, image processing and pattern recognition to analyze the real-time detection of the Kernel-like Impurity and Unsound Kernels in wheat, and a prototype was explored utilizing with technologies of mechanism, electronic and computer. The main contents are as follows:
1. Hyperspectral experiments and image acquisition system. Hyperspectral images were used to determine the optimal band according to each band's contribution to the most effective principal component. The study has found that the contribution of 630 nm band is the most useful band for morphological feature, which is right close to the wavelength of the red light used in this thesis.
2. Real-time wheat image preprocessing and feature extraction. Through the image histograms and the contrast analysis of wheat under different color backgrounds, the black and opaque background turns out to be the best one for wheat identification. After pretreatment and some image process steps, including unnecessary background cutting, homomorphic filter, image threshold segmentation, morphological image processing, color space conversion and wavelet transform,7 morphological features,27 color features and 144 texture features were extracted.
3. Comparison of different pattern recognition methods of Kernel-like impurity and Unsound Kernel in wheat. The samples include 1169 Sound Kernels,897 Kernel-like Impurities,710 Black Germ kernels, and 627 Damaged Kernels. There are several analysis algorithms used for data processing, such as genetic algorithm and the support vector machine (GA-SVM), principal component analysis and support vector machine (PCA-SVM), partial least squares discriminant analysis (PLS-DA), partial least squares and support vector machine(PLS-SVM), principal component analysis and artificial neural network (PCA-ANN), linear discriminant analysis(LDA). According to the research, combining genetic algorithm and the support vector machine (GA-SVM) is the best method, the accuracy of which was 99.34%; PCA-SVM gets the best accuracy:97.2%,98.4% and 97.9%for Black Germ Kernels, Broken Kernels and Sound Kernels.
4. Prototype development for real-time detection of Kernel-like impurity and Unsound Kernel in wheat. The mechanical part mainly includes the main stents, the single graining device, the transmission device and the material feeding device. The circuits section of the machine (lower computer) consists of core chip of ATmega128, communication circuit chip and some peripheral circuits. According to the image processing and pattern recognition method determined in experiment, the code is realized with VC++ software. The Personal computer (upper computer) can control the camera setting, image acquisition, image processing, feature extraction, pattern recognication and communication directly. The hardware and software are collaboratively debugged, and finally all software used is packaged into a setup.exe bag. Users double-click setup.exe to complete software installation and then it will be ready to work.
引文
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