Viwanda vipengele Smart Visual Vifaa vya Kugundua

Kama kampuni ya utafiti na maendeleo ya vifaa vya ufungaji wa akili ndani na nje ya nchi,Shanghai Armor Automation Teknolojia Co, LtdHuduma ya kiufundi hutoa ufumbuzi wa kiufundi kwa viwanda vya China na vifaa vya viwanda vya kimataifa vya vifaa vya uchunguzi wa kuona. Viwanda vipengele Smart Visual Vifaa vya KugunduaKutumika kwa: dawa, chakula, vinywaji, kemikali ya kila siku, huduma za afya, elektroniki, vifaa vya umeme, kemikali, viwanda vya magari na viwanda vikubwa kama vile plastiki na vifaa!

Viwanda vipengele Smart Visual UchunguzivifaakatikaTeknolojia ya usindikaji wa picha ya digital ni sekta ya teknolojia inayojitokezaTayari kuna matumizi katika mifumo ya automatisering, uchambuzi wa sehemu za magari na utambuzi wa akili. Imekuwa moja ya ufumbuzi muhimu wa kawaida ya uchunguzi wa binadamu polepole na ufanisi wa chini wa uchunguzi. Kwa sababu katika uzalishaji halisi, sehemu za viwanda zitakuwa na kasoro nyingi katika maelezo, kwa hiyo ni muhimu kuchagua algorithm sahihi ya kutambua na kuchunguza kwa usahihi. Makala hii inalenga sehemu za nyuma za sanduku la umeme wa magari, iliundwa mpango wa jumla wa mfumo wa kuchunguza picha, ilijengwa jukwaa la vifaa vya majaribio, na inaelezea kwa undani muundo wa vifaa mbalimbali na mifumo ya taa iliyotumiwa na mfumo wa kuona, kisha kufanya vipimo vya mfumo wa kamera, kukamilisha marekebisho ya athari za upotovu. Baada ya kupata picha iliyorekebishwa, utafiti ulizingatia teknolojia muhimu kama vile usindikaji wa mapema wa picha, kugundua makali, kupima vigezo vya geometry ya sehemu. Katika usindikaji wa awali, kwanza kuchambua jamii ya kelele ya picha, kulinganisha algorithms nyingi za kuchuja, na kutambua algorithms ya kuchuja inayofaa kwa picha ya makala hii. Zaidi ya hayo, katika picha edge kugundua, kulinganishwa na classic edge kugundua algorithm, kutoa msingi kwa ajili ya baadaye sifa kuchora. Wakati wa kugundua sifa za msingi za picha, mzunguko na mstari wa moja kwa moja katika picha hutambuliwa, na vigezo vya matokeo ya kupima viliboreshwa, na kuboresha athari za kugundua mzunguko na mstari wa moja kwa moja. Wakati wa kuchunguza nafasi katika picha, algorithm ya kulinganisha template ilitumiwa kutambua kwa usahihi eneo la nafasi. Baada ya kuingia katika uchunguzi wa ukubwa wa sehemu, maandishi pia alichunguza njia tatu za kutambua sehemu kamili, sehemu za welding na sehemu za scratching. Kwanza, kupitia ukaguzi wa mkoa, kwa kuhakikisha mkoa wa picha ni wazi na kamili kwa msingi, kutumia algorithm ya histogramu ya mwelekeo wa gradient kwa uchunguzi wa sifa, na kutumia mitandao ya uwezekano wa neva na SVM kwa utambuzi wa uangaliaji, na matokeo mazuri ya uangaliaji yamefanikiwa. Hata hivyo, vipimo vya vector ya kipengele ni vya juu, na habari za kuchukua kipengele zinachanganywa, hivyo habari muhimu za picha ni vigumu kutumia kikamilifu. Mabadiliko yaliyotolewa katika maandishi ya algorithm ya histogramu ya mwelekeo wa gradient, interpolation ya linear mbili ya algorithm ya kuchukua sifa ya histogramu ya mwelekeo wa gradient, ilipata vector ya sifa ambayo inaweza kuonyesha sifa za kina zaidi, kisha kutumia mitandao ya neva na mashine ya vector ya msaada kwa kutambua, wakati huo huo huo huongeza athari za kupambana na kuchanganya thamani ya sifa, pia kuboresha usahihi wa utambuzi wa aina ya picha. Utekelezaji wa modules ya somo hili ni msingi wa Visual C ++ na MATLAB, ikiwa ni pamoja na maendeleo ya interface ya mfumo wa kuona na kuandika algorithms. Makala hii inatekeleza uchunguzi wa sifa za sehemu, kutambuliwa na aina tofauti za sehemu ya makundi. Matokeo ya utafiti katika maandishi yanaonyesha thamani fulani ya uhandisi, wakati huo huo hutoa maana fulani ya matumizi ya teknolojia ya kupima picha na utambulisho wa sehemu.

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.