The Design Of Barbecue Equipment Based On Machine Vision To Judge The Cooked Degree Of Beef And Automatic Coating Sauce

In recent years,with the rapid development of artificial intelligence technology,more and more intelligent robots are involved in the catering industry,which has become an important way to solve the problem of labor cost in the catering industry.In the barbecue industry,the manual operation is mainly responsible for feeding,judging whether the food is ripe or not,brushing the sauce,feeding and so on.In this paper,according to the technical requirements of brushing sauce with specific ripen degree in the process of beef barbecue,a system based on machine vision is designed to judge the ripen degree of beef and complete the operation of brushing sauce with mechanical parts,which provides a new idea for the automatic realization of barbecue.The system is designed for the purpose of realizing the requirements of beef barbecue technology.Based on machine vision technology,the function of maturity judgment is developed.The three-axis moving mechanical parts are used to complete the action of brushing sauce.The research contents of this paper are as follows:(1)Based on the analysis of the actual process status quo of barbecue beef,the feasibility of judging the beef maturity and the operation requirements of coating sauce is realized,and the modular design of intelligent barbecue equipment for beef is carried out.In this paper,the method of judging the cooked degree of beef barbecue was studied,and a method of converting the whole temperature of beef was put forward.Design the overall scheme of the system,establish the kinematics model of brushing action,complete the design of mechanical structure and motion control scheme.(2)In order to segment the single string beef image from the complex background,image calibration distortion correction,color correction and dark channel prior smoke removal algorithm are used for pre-processing before image segmentation.In view of the problem that the Grab Cut segmentation algorithm needs to manually mark foreground pixels before each segmentation,the Grab Cut algorithm is improved by combining SLIC superpixels,and the improved Grab Cut algorithm saves manual steps.The method of minimum encasing rectangle is used to obtain the image of single string beef,and the image of single string beef is segmented from the complex background.The experiment shows that the improved Grab Cut algorithm with SLIC super pixel fusion proposed in this paper has a good effect on the barbecue beef segmentation method,with an accuracy of 88.6%,which can meet the requirements of use.(3)In order to classify the single string of beef images for their maturity,the AlexNet,VGG-16 and VGG-19 networks were selected as the classification models of beef images.In order to achieve better classification effect,the RGB images of beef were converted to LAB and HSV space and input into the three network models respectively.The experimental results showed that VGG-19 had the best performance in HSV image classification of beef,and the accuracy of judging the ripening reached 91.14%.(4)The software of the system is designed,and the accuracy of the judgment of the familiarity in the software system and the immediacy of the transmission of the control signal are verified by experiments.The repeated positioning accuracy test is carried out on the equipment.The experimental results show that the equipment can meet the requirement of repeated positioning accuracy within a certain number of working times.The software system combining the beef segmentation algorithm and the neural network model can judge the accuracy of the software system's maturity up to 86.8%,and the control signal transmission accuracy up to 100%,which can meet the requirements of use.