Study On Automatic Recognition Technology Of Mixed-flow Spray Lines Based On Machine Vision

In recent years,with the rapid economic development,more and more home appliances have entered the homes of ordinary people.The main raw material for home appliances is metal plates.In order to prolong the life of household appliances,it is usually necessary to spray a layer of anti-rust coating on the metal sheet shell.The types of products that need to be sprayed on the current spray mixed-flow production line are various.The products cannot be recognized by the machine,a lot of raw materials are wasted.This paper chooses the machine vision method to identify the product,and focuses on the software planning and hardware selection,camera distortion and image preprocessing,feature extraction and recognition algorithms,software development and experimental analysis of the visual recognition system.For the software planning and hardware selection of machine vision recognition system,the commonly used recognition algorithms of machine vision are summarized and the identification process is analyzed by referring to related documents.Then the system is divided into image acquisition module,database management module and image processing module.The subdivision of each module's composition.Then the software development platform and the image processing library were selected to develop the image preprocessing module and the software training module.Finally,the hardware needed for the vision system was selected.For camera distortion and image preprocessing,the camera image will be distorted.The common types of distortion are pincushion distortion and barrel distortion.The pinhole model based on the camera uses Zhang Zhengyou calibration method to obtain the camera's internal and external parameters by shooting the calibration plate.Correct camera distortion.The pre-processed text is combined with the plant's current environmental conditions and noise,and the appropriate algorithm is selected by comparing the image of the product being photographed with various pre-processing algorithms.For feature extraction and recognition,two identification methods are selected to extract and identify product features.The first method is based on the HU moment method.Firstly,the preprocessed template profile image is obtained and then the four moments of the profile are obtained.The variable,then the moment variable of the product to be identified is matched with the moment variable of the template to obtain the recognition result.The second method is based on the SVM method.Firstly,feature points are extracted from the acquired product images by surf algorithm.Then the feature points are clustered.Then the BOW model is constructed.Finally,the feature points are trained with the support vector machine to generate training.The model identifies the product to be identified.For the software development and recognition experiment,in order to build a good human-computer interaction interface,the visual recognition system interface is designed.The interface can visually display real-time product images,identify results,and simplify operations with buttons.Finally,a certain number of product images are tested by two identification methods.After comparing the experimental results,a better identification algorithm is selected.