Recognition And Detection Of Intrlligent Manufacturing Products Based On Machine Vision

The world manufacturing giants have put forward the strategies to revitalize the local manufacturing industry in order to develop its own manufacturing industry.From "Industry 4.0" and "Industrial Internet" to "Made in China 2025",the essence is to develop the technology,industry and application of intelligent manufacturing.In the intelligent manufacturing environment,it is required that manufacturing companies can provide customized products,and then realize large-scale,customized and flexible production.With transformation of automation and digital,factories have introduced machine vision to classify and identify products in production lines.However,the production methods of these enterprises are still push-type production methods,and the product lines produced by the production line are few,so their application machines Visual counts are also less adaptable to product categories.That cannot meet the requirements for the identification of multiple products and small batches of customized products under the conditions of smart manufacturing.What's more,this article researches product identification of machine vision in flexible production line under intelligent manufacturing environment,this paper deeply researches the matching of product and cloud data with machine vision in the flexible manufacturing environment,which mainly including improved research on matching template generation,product image preprocessing and outline extraction,research and improvement of outline feature description method,and the platform of detection and classification.Firstly,based on the background of the transition to smart manufacturing production mode,this paper proposes a method to directly use the 3D model of cloud products to generate matching templates: analyze the 3D model data of the target object and to simulate the shooting of the product under the real shooting environment with Open GL.Generate a matching template from the perspective.This method of image matching template generation can be used to improve the method of shooting production template images in current industrial production lines,and to increase the adaptability of machine vision technology to challenges in product types to meet the need to produce small-volume and multi-type products in the same production line.Secondly,this paper compares various preprocessing algorithms such as image filtering and edge detection,and chooses bilateral filtering and Canny edge detection for image preprocessing.After that,perform contour extraction on the processed binary image.Afterwards,based on the classification of features by SVM,this paper proposes and compares the Fourier description,the Fourier description of the characteristics with the combined moment,and the elliptic Fourier description algorithm to extract the contour features for classification.Generate feature descriptors of MPEG-7 graphics library with three algorithms,and then identify or predict the verification with SVM.Among that,the correct rate of SVMbased elliptical Fourier description algorithm is 90%.Finally,based on the aforementioned theoretical research,a visual inspection platform was built,and several products produced by the experiment were matched with the cloud storage data.Through experiments,the proposed SVM-based elliptic Fourier description algorithm is verified.This algorithm can meet the requirements for the classification of small and batch quantities of customized products in flexible production lines,and realize the matching of actual product and cloud product model data.