Intelligent visual inspection equipment for industrial components

As a well-known domestic and international research and development enterprise of packaging intelligent automation equipment,Shanghai Lujia Automation Technology Co., LtdOur technical services provide intelligent visual inspection equipment solutions for industrial components that are synchronized with international standards for China's manufacturing industry. Intelligent visual inspection equipment for industrial componentsapplied toMajor industries such as pharmaceuticals, food, beverages, daily chemicals, health products, electronics, electrical appliances, chemicals, automotive industry, and plastics and hardware!

Intelligent visual inspection of industrial componentsequipmentatDigital image processing technology is an emerging technology industryIt has been applied in fields such as automation systems, automotive parts testing, and intelligent recognition. It has become one of the important solutions for traditional manual detection with slow speed and low detection efficiency. Due to the numerous defects in the details of industrial parts in actual production, it is necessary to select appropriate algorithms for accurate identification and detection. This article focuses on the design of an image detection system for the back panel components of automotive energy absorbing boxes. An experimental hardware platform is built, and various components used in the vision system and the composition of the lighting system are described in detail. The camera system is then calibrated to correct distortion effects. After obtaining the corrected image, key technologies such as image preprocessing, edge detection, and measurement of geometric parameters of parts were focused on. In preprocessing, the noise category of the image was first analyzed, and various filtering algorithms were compared to find the suitable filtering algorithm for the image in this article. Furthermore, in image edge detection, classic edge detection algorithms were compared, providing a foundation for subsequent feature extraction. When detecting the basic features of an image, circles and lines in the image were separately detected, and the parameters of the detection results were optimized to improve the detection performance of circles and lines. When detecting grooves in the image, a template matching algorithm was used to accurately identify the position of the grooves. After the inspection of part dimensions, the article also studied the classification and recognition methods for intact parts, welded parts, and scratched parts. Firstly, through edge detection, while ensuring clear and complete image edges, the gradient direction histogram algorithm is used for feature extraction, and probabilistic neural networks and SVM are used for classification and recognition, achieving good classification results. However, due to the high dimensionality of feature vectors and the overlapping of feature extraction information, it is difficult to fully utilize the key information in the image. The gradient direction histogram algorithm was improved in the article by bilinear interpolation of the gradient direction histogram feature extraction algorithm to obtain feature vectors that better reflect detailed features. Then, neural networks and support vector machines were used for recognition, which not only improved the anti aliasing effect of feature values, but also increased the accuracy of image classification and recognition. The implementation of this module is based on Visual C++and MATLAB, including the development of visual system interfaces and algorithm writing. This article realizes the detection of part features and the classification and recognition of different types of parts. The research results in the article reflect certain engineering value, and provide certain reference significance for the application of image measurement technology and the classification and recognition of parts.

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.