Research on Preprocessing Technique Based on Gradient Descent for Luxury Goods Detection

With the development of the economy, more and more people are beginning to enjoy purchasing luxury goods. However, there are still many imperfect rules in the luxury goods market, which leads to many criminals exploiting loopholes in the rules to manufacture counterfeit and inferior products, greatly endangering the luxury goods market. At present, the identification of luxury goods still relies on manual labor, and in a small number of cases, software is used for identification. However, due to the complexity of the real environment, software on the market still cannot accurately identify luxury goods in complex environments. Therefore, this paper proposes a method based on the fine-grained neural network MMAL, which has the ability to recognize small object differences. The MMAL network is used as the basic network and the gradient descent method is used to preprocess the data, ensuring that luxury images can be effectively recognized and detected by the neural network. The effectiveness of the preprocessing is demonstrated through experiments.