Research On Measurement And Location Method Of Array Hole Plate Based On Binocular Vision

Array orifice is widely used in medical,biological,chemical and other fields.It is often used as the operation object of pipette and subpackage equipment.At present,the reagent bottles or liquid are ofen put into the orifice plate by artificial or related equipment,but most of the equipment is poor in generality,which is only applicable to one or several orifice plates.Therefore,the non-contact measurement and positioning of array orifice plate will greatly improve the working efficiency and intelligence of the equipment.With the extensive practice and application of machine vision in various fields,and the greater advantages of deep learning technology in target segmentation and positioning,many scholars are more and more inclined to use deep learning technology combined with traditional image processing technology to solve practical engineering application problems.At present,Specific methods are designed for specific objects on each vision measurement system,which can not meet the measurement requirements of the object in this paper.In this paper,the array orifice is taken as the object,and the area extraction,feature point extraction and stereo matching of array orifice are studied.Finally,measurement and location of array orifice are completed.For the extraction of orifice area,an improved Mask RCNN method is proposed.The improved Mask RCNN takes Mobile Net as the backbone and P4 + P5 as the FPN.Based on the research of the Mask RCNN algorithm,combined with the difficulty of segmentation and location in this paper,the improvement measures are put forward,and three kinds of backbones and different FPN layers are tested by using the self-made orifice data set.The experimental results show that the improved Mask RCNN can effectively extract the orifice area.For feature point extraction of the array hole plate,an optimized edge extraction and fitting method is proposed.Firstly,the image enhancement algorithm is studied,and the SSR method is selected for image enhancement.Secondly,the Canny edge detection algorithm is studied,and aiming at its existing problems,the common methods of each part are compared,and the Canny algorithm is improved by using bilateral filtering,four-way Sobel operator and iterative method respectively,so as to improve its edge extraction performance.Finally,according to effects of fitting of the edge fitting method(Hough transform,least square method),hough transform is used to extract straight line and circle,and the least square method is used to extract circle,and detection and merging strategy of multiple lines on the same edge and the elimination strategy of redundant circles are proposed,and the feasibility is verified by experiments.For stereo matching of array orifice,an improved SGBM method is proposed.This method uses plane fitting to eliminate the mismatching of the hole plate area,and uses the fast mean filter to fill the hole adaptively.The effectiveness of the improvement measures in the hole plate area is verified by the relevant experiments,which lays the foundation for the size measurement and positioning of the array hole plate.In the last,dimension measurement and positioning experiment of the hole plate are carried out.In terms of dimension measurement,the accuracy of the length and width of the orifice plate is up to 2mm,and the apertures and the hole distance are up to 0.5mm,which verifies the feasibility of our method and basically meets the engineering requirements;in terms of spatial positioning,the overall positioning accuracy is less than 2 mm,which needs to be further improved.The research results of this paper can promote the application of binocular vision measurement technology in the liquid workstation,provide the basis for the research and development of intelligent human-machine cooperation equipment,and promote the development of optical electromechanical integration equipment in the field of medical biochemistry.