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火腿肠外观缺陷检测技术研究
Alternative TitleResearch on Algorithms of Sausages Package Defects Detection
张勃
Department智能检测与装备研究室
Thesis Advisor马钺
Keyword机器视觉 包装缺陷检测 图像拼接 特征提取
Pages58页
Degree Discipline控制工程
Degree Name硕士
2019-05-17
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Abstract本文首先介绍了机器视觉的发展现状,之后具体介绍了常见的火腿肠外包装缺陷,针对火腿肠外包装缺陷检测之前的图像拼接问题进行了深入研究,分析了火腿肠掉扣和夹咬缺陷检测问题的难点并提出了检测方法,最后研究了火腿肠错牌识别问题并提出了解决方案。在火腿肠外包装缺陷检测系统中,获取高分辨率的火腿肠图像是缺陷检测的前提。为了提高图像分辨率,缩短相机安装位置与传送带之间的距离会导致单个相机无法拍摄到完整的火腿肠图像。针对这个问题,本文详细分析了双目视觉测量系统的原理以及图像拼接算法的理论,采用双目相机系统拍摄火腿肠的左右部分,然后利用图像拼接技术将左右两幅图拼接为一幅完整的火腿肠图像。在图像拼接过程中提出了一种基于相机标定的特征点匹配算法相对于已有的特征点匹配算法极大地加快了匹配速度。掉扣与夹咬缺陷是一类常见的火腿肠外包装缺陷。针对这一类缺陷的特点,提出了使用形状特征来识别此类缺陷。深入研究了不同的形状特征描述子之后,本文采用归一化傅里叶描述子描述火腿肠铝扣区域的形状,然后分别使用欧氏距离法和SVM(支持向量机)来识别掉扣与夹咬缺陷,并对比了两种方法的识别准确率。相对于已有的识别方法,基于傅里叶描述子和SVM的识别方法极大地提高了掉扣与夹咬缺陷的识别准确率。在火腿肠生产过程之中,由于机器故障以及工人疏忽等原因,会有一个品牌的火腿肠混入另一品牌的火腿肠的情况发生。对于火腿肠的错牌识别问题,颜色特征是最显著的一种特征。本文研究了不同品牌的火腿肠的外包装差异,提出了采用HSV颜色空间的H(色度)分量作为灰度特征。基于H分量,分别计算了均值、模糊熵、熵和各向异性4种特征量,得到一个4维的特征向量。用以上计算得到的特征向量训练多层感知机,然后用训练好的模型识别不同品牌的火腿肠,很好地解决了错牌问题。
Other AbstractFirstly, the history and actuality of machine vision inspection system was introduced in this paper, then the common defects of sausage packages was introduced in detail. Finally, the main content of this article was introduced including 3 parts: the first part is image stitching of the sausages pictures based on binocular vision, the second part is the detection of the buckle-losing defects of sausages, the third part is the classification of different brands of sausages. Obtaining high resolution images of sausages is the premise of detection of sausages package defects. In order to improve the image resolution, reducing the distance between the camera installation position and the conveyor belt will make the camera unable to capture the complete images of sausages. In order to solve this problem, the principle of binocular vision measurement system and the theory of image stitching algorithm was analyzed in detail. Using binocular cameras to take the left and right parts of the sausage and stitch them into a complete sausage image. buckle-losing defects are common defects of sausages package. For detecting this kind of defects,the idea of using shape feature to identify this kind of defects was proposed in this article. After thorough study of different shape feature descriptors and the characteristics of buckle-losing defects of sausages, Fourier descriptors were adopted in this article. Then use Euclidean distance method and SVM (Support Vector Machine) to identify buckle-losing defects respectively and compare the recognition accuracy of the two methods. Compared with the existed recognition methods, the recognition method based on Fourier descriptors and SVM greatly improves the recognition accuracy rate of buckle-losing defects of sausages. In the process of sausages production, due to machine error and workers' negligence, one brand of sausages will be mixed into another brand of sausages. The color feature is the most prominent feature for the identification of the wrong brand of sausages. In this article, the difference of sausages with different brands were well studied, and the H (chroma) component of HSV color space was extracted from sausages pictures. Based on H component, the mean, fuzzy entropy, entropy and anisotropy were calculated and combined to a four-dimensional feature vector. The multi-layer perceptron is trained with the feature vectors calculated above, and then different brands of sausages were identified using the trained model.
Language中文
Contribution Rank1
Document Type学位论文
Identifierhttp://ir.sia.cn/handle/173321/25195
Collection智能检测与装备研究室
Recommended Citation
GB/T 7714
张勃. 火腿肠外观缺陷检测技术研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2019.
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