基于DSP图像处理的鸡蛋新鲜度实时无损检测研究
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摘要
本文以TI公司的数字信号处理器TMS320DM642为核心,在硬件上,充分利用了DM642高速处理数据的能力以及实时处理特点,软件上,在CCS开发环境中,运用C语言设计图像处理及特征参数提取算法对鸡蛋透射光图像进行有效分析,分离蛋黄和气室特征,较精确的提取蛋黄与整蛋面积比值,气室面积与整蛋面积比值,气室高度与长轴比值,气室直径与短轴比值,以该四组参数分别作为无损检测参数,来寻找单一最优特征参数判别新鲜度。图像采集、图像处理算法、图像显示、优化、调试和识别过程均在DSP上实现,检测系统具有实时性。
(1)对DSP硬件平台进行了学习和应用。结合CCS软件,研究各种图像处理算法。通过视频采集、编码、处理、解码和显示一系列处理流程,开发了对鸡蛋新鲜度实时无损检测系统。
(2)获得蛋黄特征图像处理方法：通过获取鸡蛋透射光图像的G分量、灰度均衡化、中值滤波、反色、腐蚀、“与”运算、自动阈值分割等一系列图像处理方法使蛋黄特征显现,从而获得蛋黄面积与整蛋面积比,为无损检测参数提供依据。
(3)获得气室特征的图像处理方法：从鸡蛋透射光的Ⅰ分量图上可以较明显的显示气室区域,经由中值滤波、背景替换,直方图显示后,由大量实验研究得到最佳阂值分割点,进行阂值分割。再经形态学去噪,获得区域形态,对照两组形态特征分两种算法获得气室分割线的基点,进行分割,该方法分割准确,速度快。获得气室特征后,提取气室面积比、气室高度比、气室直径比。此种算法必须使气室形态均匀分布在鸡蛋大头,因此,在实际流通中检测受到限制。
(4)搭建DSP硬件平台,图像采集装置进行试验研究,选取30枚鸡蛋,每天同一时间利用软件算法提取以上四组特征参数数据,求当天各参数的平均值作为参数指标。另每天取3只同批同环境下鸡蛋测得哈夫值,求其平均值作为当天的鸡蛋哈夫值,直到对照组鸡蛋出现散黄为止,共做了29天鸡蛋新鲜度跟踪试验。
(5)利用SPSS软件建立该四组特征值与新鲜度(哈夫值)关系模型,模型相关系数高,经检验通过该四组特征模型识别鸡蛋新鲜度等级的准确率分别为91%、92%、92%、91%。
(6)将试验获取的蛋黄面积比、气室面积比、气室高度比、气室直径比四组参数分别逐一作为训练集输入样本,对应的哈夫值等级作为训练集输出样本,建立BP神经网络,最后利用验证集数据验证网络的可靠性和分级准确率,识别新鲜度等级的准确率依次为90%,89%,90%,89%。
(7)基于Visual C++6.0开发软件设计了一个鸡蛋新鲜度实时检测系统界面,将DSP处理的结果显示在PC机上,该系统实现了PC机与DSP直接通信,启动DSP后,可以实时获取经DSP处理得来的数据；实时检测每个鸡蛋的哈夫值；新鲜度等级。
考虑到当鸡蛋在运输和储存时,鸡蛋放置不正时,气室形态可能有偏移,由此呈现的气室图像不太规则,本文气室研究算法只适用于气室图像均匀分布在大头,而蛋黄面积比获得方法则不受位置限制,因此综合因素考虑,本文最终确定蛋黄面积比为图像实时无损检测参数,来鉴别鸡蛋的新鲜度,本文采用的图像处理方法可以用于鸡蛋品质在线无损检测。
通过对试验结果的分析可以得出：本文采用的对鸡蛋无损检测参数研究具有实际参考意义,并对系统的实时性进行了评价,结果表明该系统在处理速度和精度上基本满足实时性检测要求。
In this paper,take the TMS320DM642 digital signal processor of TI as the core, On hardware,the full advantage of the DM642 high-speed data processing capabilities and real-time processing features.By software,In CCS development environment.use the C language to design image processing and feature extraction algorithms.after then,to analysis the egg image under the transmitted light effectively.Separating the characteristics of egg yolk and air chamber.Extracting the ratio of yolk' area and the whole egg's area,the ratio of air chamber'area and the whole egg' area,the ratio of air chamber'height and the egg's long axis,the ratio of air chamber'diameter and the egg's short axis.Take the four parameters as the Non-destructive testing parameters one by one.To find a single optimal characteristic parameters to identify freshness.All processing such as image acquisition,image processing algorithms,image display,program optimization,debugging and recognition process are implemented on the DSP.Detection system with real-time function.
(1) DSP hardware platform for learning and application. Combination of CCS software, to study various image processing algorithms.Via video capture, encoding, processing, decoding and display a series of processes, develope an non-destructive egg freshness of real-time detection system.
(2) Image processing methods to obtain egg yolk Features:by acquisiting the egg's G component, Grayscale equalization, median filtering, anti-color, corrosion, "and" operation,automatic thresholding and a series of image processing methods to make egg yolk appear.and then,it can gain the ratio of yolk' area and the whole egg's area,that used for non-destructive testing.
(3) Image processing methods to obtain air chamber Features:from egg's I component image can be more obviously display the air chamber area.Through the median filter,background replacement,histogram displayed,by the large number of experimental studies to be the optimal threshold segmentation points for thresholding. after morphological denoising, access to regional patterns,according to two regional patterns,dividing into two kind's of algorithms to obtain air chamber partition line basis points to split.This method can cut exactly and speedly.After obtained the air chamber,to extract the ratio of air chamber's area,height,diameter. But this algorithms is restricted in the circulation of actual detection.,the air chamber evenly distributed in the egg big side.
(4) Building DSP hardware platform,and Image capture device for testing.Select 30 eggs, At the same time each day over the use of software algorithms to extract the characteristic parameters of four groups of data, Seeking the same day the average of each parameter as a parameter indicator. the other hand,take three eggs,which in the same environment and of the same batch,measured haugh value.obtain the average value as the day's eggs'haugh. until the egg appears yolk rupture.Done a total of 29 days to do tests to track the freshness of eggs.
(5) Take the use of SPSS software to establish the four characteristics of value and freshness(Hough value) relational model.Model has a very high correlation coefficient. Upon examination,through the four feature model,the accuracy of the egg s'freshness grades recognition were 91%.92%,92%,91%.
(6) With the egg yolk area ratio,air chamber area ratio,air chamber height ratio,air chamber diameter ratio accessed from tests,take them as the input training set samples one by one.other hand,take the corresponding haugh grade as the output samples,building the BP neural network.At last,use the set of validation data to test the reliability and classification accuracy of the network,,the accuracy of the egg s'freshness grades recognition were 90%,89%,90%,89%.
(7)Designed an egg freshness interface of real-time detection system based on Visual C++6.0. The results by DSP processing are showed on PC, The PC to communicate directly with the DSP by the system. After starting the DSP,It can get the data by DSP real-timely; also the eggs'haugh value and freshness grades.
When the eggs in the transport and storage,Eggs placed not straight,the air chamber shape may be offset,Then the image of air chamber also do not rule.But algorithm in this paper,assuming the air chamber is distributed evenly. And no position restrictions on accessing yolk area ratio.Therefore,a combination of factors to consider, Finally,Chosed yolk area ratio for dynamic images non-destructive testing parameters to identify the eggs'freshness grades.this method can be used online inspection.
Through the analysis of test results can be drawn:In this paper, the parameters of non-destructive testing of the eggs has practical reference value, and the system was evaluated by real-time results show that the system in the processing speed and accuracy can basically meet the requirements of real-time detection.
(1)对DSP硬件平台进行了学习和应用。结合CCS软件,研究各种图像处理算法。通过视频采集、编码、处理、解码和显示一系列处理流程,开发了对鸡蛋新鲜度实时无损检测系统。
(2)获得蛋黄特征图像处理方法：通过获取鸡蛋透射光图像的G分量、灰度均衡化、中值滤波、反色、腐蚀、“与”运算、自动阈值分割等一系列图像处理方法使蛋黄特征显现,从而获得蛋黄面积与整蛋面积比,为无损检测参数提供依据。
(3)获得气室特征的图像处理方法：从鸡蛋透射光的Ⅰ分量图上可以较明显的显示气室区域,经由中值滤波、背景替换,直方图显示后,由大量实验研究得到最佳阂值分割点,进行阂值分割。再经形态学去噪,获得区域形态,对照两组形态特征分两种算法获得气室分割线的基点,进行分割,该方法分割准确,速度快。获得气室特征后,提取气室面积比、气室高度比、气室直径比。此种算法必须使气室形态均匀分布在鸡蛋大头,因此,在实际流通中检测受到限制。
(4)搭建DSP硬件平台,图像采集装置进行试验研究,选取30枚鸡蛋,每天同一时间利用软件算法提取以上四组特征参数数据,求当天各参数的平均值作为参数指标。另每天取3只同批同环境下鸡蛋测得哈夫值,求其平均值作为当天的鸡蛋哈夫值,直到对照组鸡蛋出现散黄为止,共做了29天鸡蛋新鲜度跟踪试验。
(5)利用SPSS软件建立该四组特征值与新鲜度(哈夫值)关系模型,模型相关系数高,经检验通过该四组特征模型识别鸡蛋新鲜度等级的准确率分别为91%、92%、92%、91%。
(6)将试验获取的蛋黄面积比、气室面积比、气室高度比、气室直径比四组参数分别逐一作为训练集输入样本,对应的哈夫值等级作为训练集输出样本,建立BP神经网络,最后利用验证集数据验证网络的可靠性和分级准确率,识别新鲜度等级的准确率依次为90%,89%,90%,89%。
(7)基于Visual C++6.0开发软件设计了一个鸡蛋新鲜度实时检测系统界面,将DSP处理的结果显示在PC机上,该系统实现了PC机与DSP直接通信,启动DSP后,可以实时获取经DSP处理得来的数据；实时检测每个鸡蛋的哈夫值；新鲜度等级。
考虑到当鸡蛋在运输和储存时,鸡蛋放置不正时,气室形态可能有偏移,由此呈现的气室图像不太规则,本文气室研究算法只适用于气室图像均匀分布在大头,而蛋黄面积比获得方法则不受位置限制,因此综合因素考虑,本文最终确定蛋黄面积比为图像实时无损检测参数,来鉴别鸡蛋的新鲜度,本文采用的图像处理方法可以用于鸡蛋品质在线无损检测。
通过对试验结果的分析可以得出：本文采用的对鸡蛋无损检测参数研究具有实际参考意义,并对系统的实时性进行了评价,结果表明该系统在处理速度和精度上基本满足实时性检测要求。
In this paper,take the TMS320DM642 digital signal processor of TI as the core, On hardware,the full advantage of the DM642 high-speed data processing capabilities and real-time processing features.By software,In CCS development environment.use the C language to design image processing and feature extraction algorithms.after then,to analysis the egg image under the transmitted light effectively.Separating the characteristics of egg yolk and air chamber.Extracting the ratio of yolk' area and the whole egg's area,the ratio of air chamber'area and the whole egg' area,the ratio of air chamber'height and the egg's long axis,the ratio of air chamber'diameter and the egg's short axis.Take the four parameters as the Non-destructive testing parameters one by one.To find a single optimal characteristic parameters to identify freshness.All processing such as image acquisition,image processing algorithms,image display,program optimization,debugging and recognition process are implemented on the DSP.Detection system with real-time function.
(1) DSP hardware platform for learning and application. Combination of CCS software, to study various image processing algorithms.Via video capture, encoding, processing, decoding and display a series of processes, develope an non-destructive egg freshness of real-time detection system.
(2) Image processing methods to obtain egg yolk Features:by acquisiting the egg's G component, Grayscale equalization, median filtering, anti-color, corrosion, "and" operation,automatic thresholding and a series of image processing methods to make egg yolk appear.and then,it can gain the ratio of yolk' area and the whole egg's area,that used for non-destructive testing.
(3) Image processing methods to obtain air chamber Features:from egg's I component image can be more obviously display the air chamber area.Through the median filter,background replacement,histogram displayed,by the large number of experimental studies to be the optimal threshold segmentation points for thresholding. after morphological denoising, access to regional patterns,according to two regional patterns,dividing into two kind's of algorithms to obtain air chamber partition line basis points to split.This method can cut exactly and speedly.After obtained the air chamber,to extract the ratio of air chamber's area,height,diameter. But this algorithms is restricted in the circulation of actual detection.,the air chamber evenly distributed in the egg big side.
(4) Building DSP hardware platform,and Image capture device for testing.Select 30 eggs, At the same time each day over the use of software algorithms to extract the characteristic parameters of four groups of data, Seeking the same day the average of each parameter as a parameter indicator. the other hand,take three eggs,which in the same environment and of the same batch,measured haugh value.obtain the average value as the day's eggs'haugh. until the egg appears yolk rupture.Done a total of 29 days to do tests to track the freshness of eggs.
(5) Take the use of SPSS software to establish the four characteristics of value and freshness(Hough value) relational model.Model has a very high correlation coefficient. Upon examination,through the four feature model,the accuracy of the egg s'freshness grades recognition were 91%.92%,92%,91%.
(6) With the egg yolk area ratio,air chamber area ratio,air chamber height ratio,air chamber diameter ratio accessed from tests,take them as the input training set samples one by one.other hand,take the corresponding haugh grade as the output samples,building the BP neural network.At last,use the set of validation data to test the reliability and classification accuracy of the network,,the accuracy of the egg s'freshness grades recognition were 90%,89%,90%,89%.
(7)Designed an egg freshness interface of real-time detection system based on Visual C++6.0. The results by DSP processing are showed on PC, The PC to communicate directly with the DSP by the system. After starting the DSP,It can get the data by DSP real-timely; also the eggs'haugh value and freshness grades.
When the eggs in the transport and storage,Eggs placed not straight,the air chamber shape may be offset,Then the image of air chamber also do not rule.But algorithm in this paper,assuming the air chamber is distributed evenly. And no position restrictions on accessing yolk area ratio.Therefore,a combination of factors to consider, Finally,Chosed yolk area ratio for dynamic images non-destructive testing parameters to identify the eggs'freshness grades.this method can be used online inspection.
Through the analysis of test results can be drawn:In this paper, the parameters of non-destructive testing of the eggs has practical reference value, and the system was evaluated by real-time results show that the system in the processing speed and accuracy can basically meet the requirements of real-time detection.
引文
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