织物疵点自动检测系统关键技术的研究
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摘要
在纺织品生产中，织物的疵点检测是质量控制的重要环节，疵点的出现会影响其美观，严重影响成品的质量等级。目前，我国织物疵点的检测技术严重滞后，大多数还以人工检验为主，人为因素干扰很大，准确率不高。为此，本课题研究利用计算机图像处理技术和模式识别技术来开发有效实用的织物疵点自动检测算法。
本文以素色坯布为主要研究对象，首先对织物形成机制、织物结构特征及其图像纹理特征和织物疵点特征进行了充分分析，分析得到的成果作为先验知识嵌入到织物疵点检测算法中。为解决织物疵点自动检测这一复杂问题，从多个角度出发，以实用性和实时性为目标，设计出三种织物疵点自动检测算法。在空域设计了一种基于投影法提取特征值的织物疵点检测算法，在频域设计了一种基于织物图像频谱特征的织物疵点检测算法，利用空频分析技术设计了一种基于频域Gabor滤波器滤波的织物疵点检测算法。
本文从织物疵点自动检测系统要解决的关键技术问题出发，利用织物自身的结构特点及疵点特征等先验知识，提出一种基于投影法提取特征值的织物疵点检测算法。织物疵点的自动检测分为学习和检测两个阶段，在学习阶段，利用投影法分别在经向和纬向上提取正常织物图像的特征值，得到正常织物图像的特征数据集，用统计方法确定特征值的正常区间。在检测阶段，用异常检测的方法对提取的待检织物图像的特征值进行判断，检出疵点。通过对疵点织物图像特征值的分析表明,存在疵点的织物图像至少有一项特征值出现异常，可根据异常值出现的位置对疵点进行定位。文中采用了基于窗口的方法，对窗口大小的选择进行了探讨。实验结果表明此方法对织物图像中纱线可清晰辨识的情况比较适用，体现了方法简洁，计算快捷的优点。
本文提出了一种基于织物图像频谱特征的织物疵点检测算法，通过傅里叶变换将织物图像转换到频域，利用频域高斯滤波器对其滤波，削弱结构背景纹理，经傅里叶逆变换，再通过阈值化操作分割疵点。利用傅里叶变换得到织物图像的功率谱图，结合织物的组织结构特点，对织物图像的频谱特征进行深入分析，给出了频域高斯滤波器的参数设置方法。由于功率谱图中的峰值点可提供关于织物周期结构等重要信息，尤其是距离功率谱中心点最近的峰值点，包含纱线排列频率的信息，本文重点分析了功率谱图中的峰值点，从理论和实验上验证了峰值点和织物密度的关系，给出了利用织物密度准确定位峰值点的方法。基于频谱特征的织物疵点检测算法克服了基于投影法的疵点检测算法对织物图像中纱线不可清晰辨识的情况提取特征不稳定的问题，同时具有简单灵活的优点。通过一组机织物图像来验证织物疵点检测算法的性能，实验结果表明了此算法的可行性和有效性。
本文提出了一种基于频域Gabor滤波器滤波的织物疵点自动检测方案，详细讨论了Gabor滤波器的设计方法。本文针对织物图像的纹理特点，利用织物图像的频谱特征及疵点特征，在频域设计Gabor滤波器，确定其频域参数取值。织物图像通过Gabor滤波器滤波后，图像的结构背景纹理减弱，疵点特征突出，利用阈值法分割出疵点。利用不同类型的织物进行测试，结果证明该方法是可行和有效的，识别率可达到90%。该算法的优越之处在于充分利用织物图像的频谱特征简化了Gabor滤波器的参数设计，只要两个Gabor滤波器就可满足算法要求，减少了计算时间，对不同织物根据其频谱特征采用相应的滤波器参数，适应性强，能够满足织物疵点检测系统实时性和准确性两方面的要求。
Fabric defect detection is an important procedure for quality control of modern manufacturing in thetextile industry. Fabric defect detection is usually performed by human inspectors with a low accuracy.Hence, the effective and practical automated fabric defect detection algorithm is developed using digitalimage processing technology and pattern recognition technology in this study.
Plain fabric is taken as the main research object in this paper. Weaving mechanism, structuralcharacteristics, defect characteristics of fabric and texture characteristics of the fabric image are analyzedwhich are applied into the fabric defect detection algorithm as prior knowledge. In order to solve thecomplicated problem of automated fabric defect detection, three kinds of fabric defect detection algorithmare designed from different angles, regarding practicability and real-time as objective. The first is the fabricdefect detection algorithm based on projected transform for feature extraction in the spatial domain, thesecond is the fabric defect detection algorithm based on the spectral characteristic of fabric image in thefrequency domain, and the third is the fabric defect detection algorithm based on the Gabor filter usingspatial frequency analysis.
In order to solve the key technology problem of automated fabric defect detection, projected transformis proposed to extract features of the fabric image making use of fabric characteristic and the method ofanomaly detection is developed to detect defects in this paper. Automated fabric defect detection scheme isdivided into two phases, which are the study phase and the detection phase. During the study phase,features of normal fabric image are extracted to get the feature data set of normal fabric and the normalrange of each feature value is acquired by statistical method. During the detection phase, the method ofanomaly detection is developed using features of fabric image to detect defect and the feature values arecomputed for a set of windows covering the image. The effect of the size of the window on the fabricdefect detection algorithm is also discussed. Analysis on the feature values of the defective fabric imageshows that each of feature values is in the normal range for normal fabric image, and one feature value atleast is abnormal for defective fabric image. Defects can be located according to the location of abnormalvalue. Testing on general fabric by this method, experimental results obtained have indicated that thescheme is suitable for the fabric image that has the legible yarns, and make the calculations simple and fastfor the algorithm to be suitable for real-time applications.
The fabric defect detection algorithm based on the spectral characteristic of the fabric image isproposed in this study. The woven fabric image is operated by fast Fourier transform (FFT) and thenfiltered by the Gaussian filter designed in the frequency domain to attenuate the background texture. Afterthe inverse Fourier transform (IFT) of the output filtered image, the threshold operation is carried out tosegment defects. The power spectrum of fabric image is derived from FT function. The spectralcharacteristic of woven fabric image is analyzed in order to design Gaussian filter in the frequency domain.The periodic structures which are formed by the regular arrangement of warp and weft will result in thepeaks in the power spectrum. The first peak which is nearest to origin in the horizontal (vertical) directionhas relationship with the warp yarn (weft yarn) density of woven fabric. The physical meaning of the peaksin the power spectrum is analyzed and the relationship between peaks and fabric density is tested from bothsides of theory and experiment. The method of locating peaks accurately using fabric density is presented.The fabric defect detection algorithm based on the spectral characteristic overcomes the problem ofunstable feature extraction in the fabric defect detection algorithm based on projected transform and has theadvantage of simplicity and feasibility. The performance of the proposed algorithm has been evaluated byusing a set of woven fabric images. The experimental results have indicated that the algorithm performs very well in detecting woven fabric defects.
A Gabor filters scheme is presented for unsupervised woven fabric defect detection in this paper. Thedesign for parameters of Gabor filters in the frequency domain is studied in detail. The Gabor filters aredesigned in the frequency domain by using the prior knowledge of woven fabric structure parameters,spectral characteristic of fabric image and defect characteristics in this scheme. An input woven fabricimage is filtered in the frequency domain by the Gabor filters tuned to certain frequency and orientation,which produces an output image containing the minimum amount of background texture details whileaccentuating defect details required for defect detection. A threshold can then be performed to segmentdefects from the woven fabric image. The performance of the system is evaluated on woven fabrics withdifferent types of defects. The results indicate that the scheme is available and efficient and the truedetection rate can achieve90%. The advantage of the algorithm is that the design of Gabor filterparameters is simplified using the spectral characteristic of the fabric image. The requirements of fabricdefect detection algorithm can be satisfied by only two Gabor filters, so less time is required for filtering.Gabor filter parameters are obtained according to the woven fabric structure parameters and the spectralcharacteristic of the fabric image for different fabric. The scheme is feasible and can satisfy the real timeand accurate requirement of fabric defect detection system.
本文以素色坯布为主要研究对象，首先对织物形成机制、织物结构特征及其图像纹理特征和织物疵点特征进行了充分分析，分析得到的成果作为先验知识嵌入到织物疵点检测算法中。为解决织物疵点自动检测这一复杂问题，从多个角度出发，以实用性和实时性为目标，设计出三种织物疵点自动检测算法。在空域设计了一种基于投影法提取特征值的织物疵点检测算法，在频域设计了一种基于织物图像频谱特征的织物疵点检测算法，利用空频分析技术设计了一种基于频域Gabor滤波器滤波的织物疵点检测算法。
本文从织物疵点自动检测系统要解决的关键技术问题出发，利用织物自身的结构特点及疵点特征等先验知识，提出一种基于投影法提取特征值的织物疵点检测算法。织物疵点的自动检测分为学习和检测两个阶段，在学习阶段，利用投影法分别在经向和纬向上提取正常织物图像的特征值，得到正常织物图像的特征数据集，用统计方法确定特征值的正常区间。在检测阶段，用异常检测的方法对提取的待检织物图像的特征值进行判断，检出疵点。通过对疵点织物图像特征值的分析表明,存在疵点的织物图像至少有一项特征值出现异常，可根据异常值出现的位置对疵点进行定位。文中采用了基于窗口的方法，对窗口大小的选择进行了探讨。实验结果表明此方法对织物图像中纱线可清晰辨识的情况比较适用，体现了方法简洁，计算快捷的优点。
本文提出了一种基于织物图像频谱特征的织物疵点检测算法，通过傅里叶变换将织物图像转换到频域，利用频域高斯滤波器对其滤波，削弱结构背景纹理，经傅里叶逆变换，再通过阈值化操作分割疵点。利用傅里叶变换得到织物图像的功率谱图，结合织物的组织结构特点，对织物图像的频谱特征进行深入分析，给出了频域高斯滤波器的参数设置方法。由于功率谱图中的峰值点可提供关于织物周期结构等重要信息，尤其是距离功率谱中心点最近的峰值点，包含纱线排列频率的信息，本文重点分析了功率谱图中的峰值点，从理论和实验上验证了峰值点和织物密度的关系，给出了利用织物密度准确定位峰值点的方法。基于频谱特征的织物疵点检测算法克服了基于投影法的疵点检测算法对织物图像中纱线不可清晰辨识的情况提取特征不稳定的问题，同时具有简单灵活的优点。通过一组机织物图像来验证织物疵点检测算法的性能，实验结果表明了此算法的可行性和有效性。
本文提出了一种基于频域Gabor滤波器滤波的织物疵点自动检测方案，详细讨论了Gabor滤波器的设计方法。本文针对织物图像的纹理特点，利用织物图像的频谱特征及疵点特征，在频域设计Gabor滤波器，确定其频域参数取值。织物图像通过Gabor滤波器滤波后，图像的结构背景纹理减弱，疵点特征突出，利用阈值法分割出疵点。利用不同类型的织物进行测试，结果证明该方法是可行和有效的，识别率可达到90%。该算法的优越之处在于充分利用织物图像的频谱特征简化了Gabor滤波器的参数设计，只要两个Gabor滤波器就可满足算法要求，减少了计算时间，对不同织物根据其频谱特征采用相应的滤波器参数，适应性强，能够满足织物疵点检测系统实时性和准确性两方面的要求。
Fabric defect detection is an important procedure for quality control of modern manufacturing in thetextile industry. Fabric defect detection is usually performed by human inspectors with a low accuracy.Hence, the effective and practical automated fabric defect detection algorithm is developed using digitalimage processing technology and pattern recognition technology in this study.
Plain fabric is taken as the main research object in this paper. Weaving mechanism, structuralcharacteristics, defect characteristics of fabric and texture characteristics of the fabric image are analyzedwhich are applied into the fabric defect detection algorithm as prior knowledge. In order to solve thecomplicated problem of automated fabric defect detection, three kinds of fabric defect detection algorithmare designed from different angles, regarding practicability and real-time as objective. The first is the fabricdefect detection algorithm based on projected transform for feature extraction in the spatial domain, thesecond is the fabric defect detection algorithm based on the spectral characteristic of fabric image in thefrequency domain, and the third is the fabric defect detection algorithm based on the Gabor filter usingspatial frequency analysis.
In order to solve the key technology problem of automated fabric defect detection, projected transformis proposed to extract features of the fabric image making use of fabric characteristic and the method ofanomaly detection is developed to detect defects in this paper. Automated fabric defect detection scheme isdivided into two phases, which are the study phase and the detection phase. During the study phase,features of normal fabric image are extracted to get the feature data set of normal fabric and the normalrange of each feature value is acquired by statistical method. During the detection phase, the method ofanomaly detection is developed using features of fabric image to detect defect and the feature values arecomputed for a set of windows covering the image. The effect of the size of the window on the fabricdefect detection algorithm is also discussed. Analysis on the feature values of the defective fabric imageshows that each of feature values is in the normal range for normal fabric image, and one feature value atleast is abnormal for defective fabric image. Defects can be located according to the location of abnormalvalue. Testing on general fabric by this method, experimental results obtained have indicated that thescheme is suitable for the fabric image that has the legible yarns, and make the calculations simple and fastfor the algorithm to be suitable for real-time applications.
The fabric defect detection algorithm based on the spectral characteristic of the fabric image isproposed in this study. The woven fabric image is operated by fast Fourier transform (FFT) and thenfiltered by the Gaussian filter designed in the frequency domain to attenuate the background texture. Afterthe inverse Fourier transform (IFT) of the output filtered image, the threshold operation is carried out tosegment defects. The power spectrum of fabric image is derived from FT function. The spectralcharacteristic of woven fabric image is analyzed in order to design Gaussian filter in the frequency domain.The periodic structures which are formed by the regular arrangement of warp and weft will result in thepeaks in the power spectrum. The first peak which is nearest to origin in the horizontal (vertical) directionhas relationship with the warp yarn (weft yarn) density of woven fabric. The physical meaning of the peaksin the power spectrum is analyzed and the relationship between peaks and fabric density is tested from bothsides of theory and experiment. The method of locating peaks accurately using fabric density is presented.The fabric defect detection algorithm based on the spectral characteristic overcomes the problem ofunstable feature extraction in the fabric defect detection algorithm based on projected transform and has theadvantage of simplicity and feasibility. The performance of the proposed algorithm has been evaluated byusing a set of woven fabric images. The experimental results have indicated that the algorithm performs very well in detecting woven fabric defects.
A Gabor filters scheme is presented for unsupervised woven fabric defect detection in this paper. Thedesign for parameters of Gabor filters in the frequency domain is studied in detail. The Gabor filters aredesigned in the frequency domain by using the prior knowledge of woven fabric structure parameters,spectral characteristic of fabric image and defect characteristics in this scheme. An input woven fabricimage is filtered in the frequency domain by the Gabor filters tuned to certain frequency and orientation,which produces an output image containing the minimum amount of background texture details whileaccentuating defect details required for defect detection. A threshold can then be performed to segmentdefects from the woven fabric image. The performance of the system is evaluated on woven fabrics withdifferent types of defects. The results indicate that the scheme is available and efficient and the truedetection rate can achieve90%. The advantage of the algorithm is that the design of Gabor filterparameters is simplified using the spectral characteristic of the fabric image. The requirements of fabricdefect detection algorithm can be satisfied by only two Gabor filters, so less time is required for filtering.Gabor filter parameters are obtained according to the woven fabric structure parameters and the spectralcharacteristic of the fabric image for different fabric. The scheme is feasible and can satisfy the real timeand accurate requirement of fabric defect detection system.
引文
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