.Product Quality Detection and Recognition based on Vision and Deep Learning

In order to address the defective surface of products in traditional manufacturing, the detection efficiency and accuracy of defects should be improved to sort out products effectively. The combination of in-depth learning and machine vision technology can realize accurate detection of product quality. In the first place product data and images that were collected through machine vision and the illuminating system were uploaded to the cloud. And then, by analyzing such data and images, a convolutional neural network model was built and used to train and divide test sets through in-depth learning. At last, then this model was applied to check sample products for defective surfaces and classify types of defects with high accuracy and efficiency. Machine vision and cloud can collect and analyze quality data, while in-depth learning improves detection accuracy.