Ajuiɛɛr ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic

A ye raan ŋic bɛ̈ɛ̈i dɛ̈t bɛ̈ɛ̈i dɛ̈t bɛ̈ɛ̈i dɛ̈t bɛ̈ɛ̈i dɛ̈t,Shanghai Lu Ka Automation Technology Co., Ltd. (Shanghai Lu Ka Automation Technology Co., Ltd.)Teknoloji ye kɛ̈n ye kɛ̈n ye kɛ̈n ye kɛ̈n ye kɛ̈n ye kɛ̈n ye kɛ̈n ye kɛ̈n ye kɛ̈n ye kɛ̈n ye kɛ̈n ye kɛ Ajuiɛɛr ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋic ŋicLuɔi: Pharmaceutical, food, drink, daily chemical, health care products, electronics, electrical appliances, chemical, automotive industry and plastic and hardware industries and so forth!

Intelligent Visual Detection of Industrial Parts (Kɔl-kɛl-kɛl-kɛl-kɛl-kɛl-kɛl-kɛl-kɛl-kɛl-kɛl-kɛl-kɛl)ApparatuHaDigital Image Processing Technology ye teknoloji ye bɛ̈n looiA tɔ̈u thïn në ɣän ke automation systems, car parts detection ku smart recognition. A kɛra laan ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l. Lɔnadɛ̈ lɔnadɛ̈ lɔnadɛ̈ lɔnadɛ̈ lɔnadɛ̈ lɔnadɛ̈ lɔnadɛ̈ lɔnadɛ̈ lɔnadɛ̈ lɔnadɛ̈ lɔnadɛ̈ lɔnadɛ̈ lɔnadɛ̈ lɔnadɛ̈ lɔ Article kënë ye kɛ̈ɛ̈l ye kɛ̈ɛ̈l ye kɛ̈ɛ̈l ye kɛ̈ɛ̈l ye kɛ̈ɛ̈l ye kɛ̈ɛ̈l ye kɛ̈ɛ̈l ye kɛ̈ɛ̈l ye kɛ̈ɛ̈l ye kɛ̈ɛ̈l ye kɛ̈ɛ̈l ye kɛ̈ɛ̈l Ka fotow tɔ̈ɔ̈u, a cï puɔ̈ɔ̈c bɛ̈n looi në tɛ̈n yenë fotow tɔ̈ɔ̈u thïn, ku jɔl ya tɛ̈n yenë kɔc ye kɔc ye kɔc ye kɔc ye kɔc ye kɔc ye kɔc ye k Në luɔɔi tueŋ de foto, a fɔlɔ ka tɛ̈n bɛ̈n ya tɛ̈n bɛ̈n ya tɛ̈n bɛ̈n ya tɛ̈n bɛ̈n ya tɛ̈n bɛ̈n ya tɛ̈n bɛ̈n ya Ku bɛ̈ɛ̈n, ka tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛm Tɛ̈n yenë fotow tɔ̈u thïn, a bɛ̈n ya tɔ̈u thïn, a bɛ̈n ya tɔ̈u thïn, a bɛ̈n ya tɔ̈u thïn, a bɛ̈n ya tɔ̈u thïn, a bɛ̈n ya tɔ̈u thïn, Ka tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ t Ka tɛmɛ, ka tɛmɛ, ka tɛmɛ, ka tɛmɛ, ka tɛmɛ, ka tɛmɛ, ka tɛmɛ, ka tɛmɛ, ka tɛmɛ, ka tɛmɛ, ka tɛmɛ, ka tɛmɛ Fɔlɔ, ka tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛmɛ tɛ Ku na yïn ya, kä ye kek tɔ̈ɔ̈u aye kä ye kek tɔ̈ɔ̈u, kä ye kek tɔ̈ɔ̈u aye kä ye kek tɔ̈ɔ̈u, ku kä ye kek tɔ̈ɔ̈u aye kä ye kek tɔ̈ɔ̈u A ye gradient direction histogram algorithm looi, a ye gradient direction histogram feature extraction algorithm looi, a ye bi-linear interpolation looi, a ye characteristic vector looi, a ye characteristic vector looi, a ye characteristic vector looi, a ye characteristic vector looi, a ye characteristic vector looi, a ye characteristic vector looi, a ye characteristic vector looi, a ye characteristic vector looi, a ye characteristic vector looi, a ye characteristic vector looi, a ye characteristic vector looi, a ye characteristic vector looi Module kënë bɛ̈n tɔ̈u në Visual C++ ku MATLAB, agut cï Visual System Interface Development ku Algorithms Writing. Article kënë ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l ye kɛ̈l Kɔc cï gɔ̈ɔ̈r në athöör kënë yic aye kä puɔth ye kek looi në luɔɔi de tɛ̈n yenë kek tɛ̈n yenë kek tɛ̈n yenë kek tɛ̈n yenë kek tɛ̈n yenë kek tɛ̈

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.