基于视觉感知的带钢表面缺陷检测与识别
发布时间：2018-12-18 02:35
【摘要】：带钢产品,作为钢铁产品中的一个重要部分,已经成为航空航天、机械制造、汽车生产、化工等工业的重要原材料,其质量直接影响着产品的最终性能。因此,作为带钢表面质量自动评估的一种重要手段,基于机器视觉的带钢表面缺陷在线检测的研究具有重要的理论与现实意义。针对现有常规缺陷检测方法在实际应用中吞吐量低、检测准确率不高的问题,本文从基于纹理异常检测和机器学习两方面着手,分别提出了基于局部二值模式(LBP)的缺陷纹理异常检测和基于卷积神经网络CNN的缺陷检测方法。同时,为了提高分类算法的准确性,提出一种基于特征对的改进ReliefF特征选择方法。本文研究的内容与成果如下:(1)针对传统检测方法在带钢缺陷检测中误检率和漏检率较高,检测算法参数较多的问题,提出了一种基于多尺度LBP编码的缺陷检测方法。在不同尺度下对带钢表面图像创建高斯差分金字塔模型,确定缺陷疑似区域,然后针对可疑区域进行经过阈值化处理后的LBP编码,最后将所有尺度下的编码图像融合,生成最终的像素级缺陷位置信息,将检测结果中的连通域合并(ROI合并),生成完整的缺陷位置信息。(2)为了排除背景纹理的干扰,抑制伪缺陷的产生,本文再次提出一种基于奇异值分解的缺陷检测方法SVD-LBPH,通过对待检测图像进行SVD分解与重构,弱化背景纹理,然后采用LBP对图像进行编码,提取LBP直方图相关的统计特征,通过将特征与设定的阈值比较,最终检测出缺陷。(3)实时检测对实时性要求较高,实时检测结束后,需要在检测出来的缺陷区域进行即时检测,进一步剔除伪缺陷。在即时检测阶段,针对深度学习算法在目标检测与图像分类的高准确率,采用卷积神经网络CNN进行缺陷的检测,通过与其它基于机器学习的缺陷检测方法进行对比,验证了卷积神经网络在缺陷检测的高效潜力。(4)为了对缺陷类别进行辨别,本文提取了缺陷的灰度特征、灰度共生阵以及频域等特征。为了提高分类准确率,剔除不相关的特征,同时为了避免因特征维数过大而造成的过拟合,采用了特征筛选的手段。本文利用更新特征对权重的方式对ReliefF进行改进,实现特征的降维。最后利用SVM对筛选后的特征进行分类。
[Abstract]:Strip products, as an important part of iron and steel products, have become an important raw material in aerospace, mechanical manufacturing, automobile production, chemical industry, etc. The quality of strip products directly affects the final performance of the products. Therefore, as an important means of automatic evaluation of strip surface quality, the research of on-line detection of strip surface defects based on machine vision has important theoretical and practical significance. Aiming at the problems of low throughput and low detection accuracy of the existing conventional defect detection methods in practical applications, this paper starts from two aspects: texture-based anomaly detection and machine learning. Defect texture anomaly detection based on local binary mode (LBP) and defect detection based on convolution neural network CNN are proposed respectively. At the same time, in order to improve the accuracy of the classification algorithm, an improved ReliefF feature selection method based on feature pairs is proposed. The contents and achievements of this paper are as follows: (1) aiming at the problems of high false detection rate and high miss detection rate and more parameters of detection algorithm, a new defect detection method based on multi-scale LBP coding is proposed. Gao Si differential pyramid model is created for the strip surface image at different scales to determine the suspected defect area, and then the LBP coding after threshold processing is carried out for the suspected area. Finally, the coding image at all scales is fused. The final pixel level defect location information is generated, and the connected domain (ROI merging) in the detection result is combined to generate the complete defect location information. (2) in order to eliminate the interference of background texture, the false defect is suppressed. In this paper, a new defect detection method based on singular value decomposition (SVD) is proposed, which weakens background texture by SVD decomposition and reconstruction of detected image, and then uses LBP to encode the image and extract the statistical features related to LBP histogram. By comparing the features with the set threshold, the defects are finally detected. (3) Real-time detection requires high real-time performance. After the real-time detection, it is necessary to detect the defects in the detected areas immediately, and further eliminate the false defects. In the phase of immediate detection, aiming at the high accuracy of depth learning algorithm in target detection and image classification, a convolutional neural network (CNN) is used to detect defects, which is compared with other defect detection methods based on machine learning. The high efficiency potential of convolution neural network in defect detection is verified. (4) in order to distinguish the defect category, the gray level feature, gray level co-occurrence matrix and frequency domain feature of defect are extracted in this paper. In order to improve classification accuracy, eliminate irrelevant features, and avoid over-fitting caused by large feature dimension, feature selection is adopted. In this paper, ReliefF is improved by updating the weight of feature pair to reduce the dimension of feature. Finally, SVM was used to classify the selected features.
【学位授予单位】：河北工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TG142.15;TP391.41
[Abstract]:Strip products, as an important part of iron and steel products, have become an important raw material in aerospace, mechanical manufacturing, automobile production, chemical industry, etc. The quality of strip products directly affects the final performance of the products. Therefore, as an important means of automatic evaluation of strip surface quality, the research of on-line detection of strip surface defects based on machine vision has important theoretical and practical significance. Aiming at the problems of low throughput and low detection accuracy of the existing conventional defect detection methods in practical applications, this paper starts from two aspects: texture-based anomaly detection and machine learning. Defect texture anomaly detection based on local binary mode (LBP) and defect detection based on convolution neural network CNN are proposed respectively. At the same time, in order to improve the accuracy of the classification algorithm, an improved ReliefF feature selection method based on feature pairs is proposed. The contents and achievements of this paper are as follows: (1) aiming at the problems of high false detection rate and high miss detection rate and more parameters of detection algorithm, a new defect detection method based on multi-scale LBP coding is proposed. Gao Si differential pyramid model is created for the strip surface image at different scales to determine the suspected defect area, and then the LBP coding after threshold processing is carried out for the suspected area. Finally, the coding image at all scales is fused. The final pixel level defect location information is generated, and the connected domain (ROI merging) in the detection result is combined to generate the complete defect location information. (2) in order to eliminate the interference of background texture, the false defect is suppressed. In this paper, a new defect detection method based on singular value decomposition (SVD) is proposed, which weakens background texture by SVD decomposition and reconstruction of detected image, and then uses LBP to encode the image and extract the statistical features related to LBP histogram. By comparing the features with the set threshold, the defects are finally detected. (3) Real-time detection requires high real-time performance. After the real-time detection, it is necessary to detect the defects in the detected areas immediately, and further eliminate the false defects. In the phase of immediate detection, aiming at the high accuracy of depth learning algorithm in target detection and image classification, a convolutional neural network (CNN) is used to detect defects, which is compared with other defect detection methods based on machine learning. The high efficiency potential of convolution neural network in defect detection is verified. (4) in order to distinguish the defect category, the gray level feature, gray level co-occurrence matrix and frequency domain feature of defect are extracted in this paper. In order to improve classification accuracy, eliminate irrelevant features, and avoid over-fitting caused by large feature dimension, feature selection is adopted. In this paper, ReliefF is improved by updating the weight of feature pair to reduce the dimension of feature. Finally, SVM was used to classify the selected features.
【学位授予单位】：河北工业大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TG142.15;TP391.41
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