Research On Key Technology Of Car Seat Back Detection Based On Machine Vision

This paper mainly studies the key technology of vehicle seat backrest detection based on machine vision.According to the production environment and corresponding requirements of the actual industrial site,the detection system of "image processing software of single chip microcomputer of vision sensor" is developed.The system can detect the type of car seat backrest and the missing parts of seat backrest spring hook.The main research contents include the selection of hardware and the construction of experimental platform,the pre-processing of image acquisition,the research of missing detection algorithm of automobile seat backrest type and spring hook parts.(1)Pre-processing of collected images.The original image of the car seat backrest image collected by the camera is preprocessed by using three different filtering methods: median filter,mean filter and Gaussian filter.the values of the corresponding mean variance(MSE)and peak signal-to-noise ratio(PSNR)are calculated respectively for the three filtering modes and the same filtering mode under the selection of different templates.for the application of this paper,the mean variance of Gaussian filtering under 5*5 template is :19.0753,and the peak signal-to-noise ratio is :35.3261,which is better than the quality of the image processed by other filtering methods.In order to highlight the "small spring" feature in the background of "big backrest ",Laplace sharpening method is used to increase the edge feature of spring hook parts,which is convenient for the later automobile seat backrest type and the leakage detection of spring hook parts.(2)An improved RANSAC-SURF algorithm is proposed to detect the type of car seat backrest.First of all,according to SURF traditional feature point detection and matching algorithm,the vehicle seat backrest is matched with feature points.and then the SURF feature points are screened and detected and matched by a random sampling consistent algorithm.Finally,the improved random sampling consistent algorithm is used to screen and match SURF feature points.Because in the actual situation,the matching distance between the correct feature points is basically close.As a result,the improved RANSAC-SURF algorithm proposed to filter the pre-matching points by comparing the matching distance between the feature points and the set value.According to the statistics of the matching logarithm of the total feature points and the number of intersection points of the lines between the featurepoints formed by the error matching,it is concluded that the total characteristic point matching logarithm of the improved RANSAC-SURF matching algorithm is 188 and the number of intersection points is 18.This shows that the correct rate of matching is higher than the detection and matching algorithm of the first two feature points,and the reduction of the logarithm of the total matching points can also improve the speed of the system matching.(3)To detect the missing position of the backrest spring hook parts.First,the distortion correction of the collected seat backrest image is carried out by perspective transformation,and then the template position image is set for the eight spring hooks position of the seat backrest.Finally,according to the image collected by the camera and the image of the normal installation spring hook,the absolute value of the gray difference at the set template position is compared with the set threshold value to determine whether there is leakage and the position of leakage of the seat back spring hook.The experimental results show that the absolute gray difference is between 33 and 52 at the missing spring position,and the absolute gray difference is not higher than 10 at the normal spring installation position.So set the absolute gray difference threshold of 20,which can well detect the seat spring hook parts leakage problem.It is proved that the proposed algorithm can detect accurately the type of car seat backrest and the leakage of spring hook stably and effectively,including software development environment,design and implementation of test platform structure and evaluation of test result.By 100% detection accuracy,it shows that the device can meet the needs of the actual industrial field.