Industrial machine vision system based on improved sensing detection device

Abstract

The invention discloses an industrial machine vision system based on an improved sensing detection device. The system comprises a sampling viewing system, a displacement device and a feedback linkingsystem which are connected one another with data transmission and control transmission, wherein the viewing system comprises a top viewing component, a lateral viewing component, a top motion controldevice, a lateral motion control device and a light reflection-based high-precision measurement device; the displacement device comprises a horizontal expansion cross rod, a vertical expansion stand column, a rotation adjustment device and a position movement component; and the feedback linking system comprises a feedback linking treatment device and a feedback linking control device. According tothe industrial machine vision system based on the improved sensing detection device, the whole system moves along with the process of a to-be-detected industrial processing production line through the viewing component and the displacement device, the process precision of the processing production line can be monitored, the production line action can be reversely warned or corrected, and the problem generated in the production process can be adjusted.

Description

technical field [0001] The invention discloses an industrial machine vision system based on an improved sensor detection device, and relates to the technical field of machine vision. Background technique [0002] Machine vision is a rapidly developing branch of artificial intelligence. Simply put, machine vision is the use of machines instead of human eyes for measurement and judgment. The machine vision system converts the captured target into an image signal through the machine vision product (that is, the image capture device, which is divided into CMOS and CCD), and transmits it to a dedicated image processing system to obtain the shape information of the captured target. According to the pixel distribution And brightness, color and other information are converted into digital signals; the image system performs various operations on these signals to extract the characteristics of the target, and then controls the on-site equipment actions according to the results of the...

AI Technical Summary

Problems solved by technology

[0004]The invention patent of CN104971973B "A Car Wheel Cover Hemming System and Its Using Method" mentions the application of the vision system, but it is limited to the positioning function of the vision system

However, its technical solution is limited to the pre-set trajectory optimization, which cannot solve the problem that the traditional visual inspection system based on a single camera cannot cover 360 degrees, and defect detection cannot be realized in some areas

[0006]The technical solution disclosed in CN201810494716's invention patent "A Machine Vision Inspection and Screening Device and Inspection System Based on Line Scan Camera" realizes multi-angle coverage inspection , but this technical solution needs to install a specific rotating mechanism on the production line to detect defects around the object, but it is inconvenient to use, and the work efficiency is low, and the defects on the upper surface of the object cannot be detected, so it cannot truly realize all-round defect detection

[0007] In addition, the application of machine vision technology in industrial systems in the prior art still has the following problems: With the development of science and technology, parts in the industrial field The structure is becoming more and more complex, and various defects in micro-pipes or small-sized characteristic inner holes are difficult to be observed due to the detection accuracy and detection orientation.

[0008] Conventional micro-pipe hole detection is to detect micro-pores after the detection sensor is miniaturized, such as using a micro-endoscope or a CCD camera to enter the micro-hole to collect images of the inner surface , to identify defects by artificial or image processing algorithms, but these methods can only judge the presence or absence of defects, and cannot obtain their three-dimensional information

Can only qualitatively judge the internal defect information of the tube hole, and cannot realize quantitative detection of the inner wall defect of the tube hole

Images