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工业零部件智能视觉检测设备

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工业零部件智能视觉检测设备

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工业零部件智能视觉检测设备

 

       作为国内外知名包装智能自动化设备研发企业,上海陆甲自动化科技有限公司的技术服务为中国制造业提供了与国际同步工业零部件智能视觉检测设备技术解决方案。工业零部件智能视觉检测设备应用于:制药、食品、饮料、日化、保健品、电子、电器、化工、汽车工业及塑料与五金等各大行业!

  

       工业零部件智能视觉检测设备数字图像处理技术是一个新兴的技术行业,已在自动化系统、汽车零部件检测和智能识别等领域都有的应用。它已经成为传统人工检测速度慢、检测效率低的重要解决办法之一。由于实际生产中,工业零件在细节方面会有诸多缺陷,因此,有必要选用合适的算法对其进行准确的识别和检测。本文针对汽车吸能盒背板零件,设计了图像检测系统的整体方案,搭建了实验硬件平台,并详细介绍了视觉系统采用的各种器件和照明系统的组成,再进行摄像系统标定,完成了畸变效应的矫正。在获取矫正后的图像后,对图像的预处理、边缘检测、零件几何参数测量等关键技术进行了重点研究。在预处理中,首先分析了图像的噪声类别,比较了多种滤波算法,找出适合本文图像的滤波算法。进而,在图像边缘检测中,对比了经典的边缘检测算法,为后续的特征提取提供了基础。在检测图像基本特征时,分别检测图像中的圆和直线,并对检测结果的参数进行了优化,提高了圆和直线的检测效果。在对图像中的槽进行检测时,采用了模板匹配算法,对槽的位置进行了准确的识别。在进了了零件尺寸的检测之后,文中还研究了完好零件、焊点零件和划痕零件三种情况的分类识别方法。首先,通过边缘检测,在保证图像边缘清晰、完整的基础上,利用梯度方向直方图算法进行特征提取,并采用概率神经网络和SVM进行分类识别,取得了不错的分类效果。然而,特征向量维度较高,特征提取信息混叠,以致图像关键信息难以充分利用。文中对梯度方向直方图算法进行了改进,对梯度方向直方图特征提取算法进行双线性插值,得到了更能够体现细节特征的特征向量,再用神经网络和支持向量机进行识别,在提高特征值抗混叠效应的同时,也提高了图像的分类识别准确率。本课题模块的实现都是基于Visual C++和MATLAB的,包括视觉系统界面开发和算法的编写。本文实现了零件特征的检测,与不同种类的零件分类识别。文中的研究结果体现了一定的工程价值,同时对图像测量技术的应用和零件的分类识别提供一定的借鉴意义。

 

 

Intelligent visual inspection equipment

 

As a well-known packaging intelligent automation equipment research and development enterprise at home and abroad, Shanghai Lujia Automation Technology Co., Ltd. provides technical solutions for the Chinese manufacturing industry to synchronize intelligent visual inspection equipment for industrial parts. Widely used in: pharmaceutical, food, beverage, daily chemical, health care products, electronics, electrical appliances, chemicals, automotive industry and plastics and hardware industries!

 

Intelligent visual inspection equipment for industrial components is an emerging technology industry in digital image processing technology. It has been widely used in automation systems, automotive parts inspection and intelligent identification. It has become one of the important solutions for slow manual detection and low detection efficiency. Due to the defects in the details of industrial parts in actual production, it is necessary to use an appropriate algorithm to accurately identify and detect them. In this paper, the overall scheme of the image detection system is designed for the back part of the car energy-absorbing box. The experimental hardware platform is built, and the components of the various components and lighting systems used in the vision system are introduced in detail. Then the camera system is calibrated and completed. Correction of distortion effects. After obtaining the corrected image, key technologies such as image preprocessing, edge detection and part geometric parameter measurement were studied. In the preprocessing, the noise class of the image is first analyzed, and various filtering algorithms are compared to find the filtering algorithm suitable for the image. Furthermore, in the image edge detection, the classic edge detection algorithm is compared, which provides the basis for the subsequent feature extraction. When detecting the basic features of the image, the circles and lines in the image are detected separately, and the parameters of the detection result are optimized to improve the detection effect of the circle and the line. When detecting the slot in the image, a template matching algorithm is used to accurately identify the position of the slot. After the inspection of the part size, the classification and identification methods of the intact parts, the solder joint parts and the scratch parts were also studied. Firstly, through the edge detection, on the basis of ensuring the image edge is clear and complete, the gradient direction histogram algorithm is used for feature extraction, and the probabilistic neural network and SVM are used for classification and recognition, and a good classification effect is obtained. However, the feature vector dimension is high, and the feature extraction information is aliased, so that the key information of the image is difficult to fully utilize. In this paper, the gradient direction histogram algorithm is improved, and the gradient direction histogram feature extraction algorithm is bilinearly interpolated. The feature vector which can reflect the detailed features is obtained, and then the neural network and support vector machine are used for recognition. The anti-aliasing effect of the value also improves the accuracy of classification and recognition of images. The implementation of all modules of this topic is based on Visual C++ and MATLAB, including visual system interface development and algorithm writing. This paper realizes the detection of part features and the classification and identification of different types of parts. The research results in this paper reflect a certain engineering value, and provide some reference for the application of image measurement technology and the classification and identification of parts.


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