Research And Application Of Machine Vision Measurement Technology

In recent years,machine vision measurement technology has solved the problem that traditional measurement methods can not satisfy the society's requirements for measurement technology and with its features of rapidity,easy operation,no contact,high precision,and automation,and it is easier to improve the accuracy and speed of measurement systems by improving image processing techniques,making machine vision measurement technology gradually occupied the dominant position in the field of measurement.The main manifestations of machine vision technology is binocular vision,this paper conducts in-depth research from this aspects,based on binocular stereovision measurement system and the Laboratory's project of a beam monitor using silicon pixel sensors for hadron therapy,set up a respiratory motion tracking system for tracking respiratory movements of patients during cancer radiotherapy to avoid normal target tissue damage caused by displacement of respiratory target during radiotherapy and affect radiotherapy effect.It is proposed to use real-time tracking of the movement status of the body surface marker to feedback the patient's respiratory motion and to infer whether the state of respiratory motion leads to displacement of the tumor target.As the system generally uses a fixed focus camera,manual focusing operation is required,image clarity is generally judged by artificial subjective awareness.The accuracy of the system is not high due to certain errors due to manual operations,It is of practical significance to build an auto-focus system to automate the image focusing quickly and accurately to improve the measurement system composed of these cameras.For this reason,this paper research the key technologies for realizing the binocular vision real-time tracking system and the autofocus system.First of all,this article has carried on the overall design to the system,has carried on the analysis to the key technology among them;Then,through the research and simulation of traditional sub-pixel edge detection algorithms,draw a detection line in the direction of the edge point normal,positioning edge sub-pixels using the gray-scale relationship of pixel points on the detection line.Then,for the multiple rectangular markers used in this system,combining corner features and marker arrangement features,a recognition algorithm based on the features of multiple rectangular markers was researched.Then,for these multiple rectangular markers,proposes a matching algorithm that uses three-dimensional matching with the inner areaprimitive.Then,for focusing systems,utilizing the sharp relationship between the sharpness of edges and the sharpness of the image to put forward an optimized focus of each rectangular marker as the evaluation function based on edge features.Finally,a real-time tracking system based on binocular vision and an automatic focusing system were developed.the system of a real-time tracking system based on binocular vision,the system can accomplish the functions of image acquisition,camera stereo calibration,image radial distortion and tangential distortion calibration,median filter denoising,image target recognition,image stereo matching,target three-dimensional information acquisition and interface stereo display.The auto-focus system is constructed through components such as a single-chip microcomputer,a motor,and a camera to achieve automatic image focusing.The system has been completed.After preliminary tests,the various functional modules of the system have been implemented.the edge detection sub-pixel algorithm's accuracy can reach 0.02 pixels;The target recognition algorithm and stereo matching algorithm have a processing time of 2.56ms and 30ms per frame,respectively;This focus evaluation functions sensitive to grayscale transitions,the algorithm is simple,and the amount of calculation is small,Better than classic evaluation function for changes in image clarity,faster A real-time tracking system based on binocular vision make the measurement error of the system lower than±1mm,andtakes 35ms to process each frame of image.The real-time and accuracy of the algorithm and system reach the predetermined goal.