Product surface quality inspection method based on VGG16-SVM-SSA
Aiming at the problems of missing detection,wrong detection and low recognition efficiency in traditional visual detection methods,a product surface quality detection method based on deep learning was proposed to acceler-ate the detection efficiency of production line and improve the intelligent level of quality control.Starting from the basic process of product surface quality inspection,the product surface quality problem modeling was carried out.On this basis,an improved vgg16 network model was constructed for image recognition,which used Support Vector Machine(SVM)to replace the softmax classifier in VGG16 network model,and applied Sparrow Search Algorithm(SSA)to further optimize the super parameters of SVM,so as to enhance the classification accuracy of the mod-el.At the same time,the image defect feature knowledge base was built and the surface defect data system of stand-ard products was developed.Finally,the quality detection system of industrial cloud platform based on deep learning was designed and developed to realize the efficient interactive connection between production line,equipment and personnel,as well as the real-time collection,transmission,intelligent detection and data management of product surface quality data.The feasibility of the proposed model and method proposed was verified by the case of cast im-peller.
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维普
