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The study of FMCG recognition system based on deep learning is of great significance in improving the logistics efficiency of FMCG products and the speed of warehousing in and out of the warehouse. This paper analyzes the research and development status of image-based FMCG recognition system in China and other countries, and aims to design a FMCG recognition system based on deep learning, including fully connected neural network and convolutional neural network, which can meet the requirements of recognizing specific FMCG products after training datasets, and the recognition accuracy is in line with expectations. In this paper, after determining the overall structure of this FMCG recognition system, the fully connected neural network, convolutional neural network, pooling layer and pooling function were learned accordingly, and adopted the two optimization methods: Regularization and Dropout, to avoid the occurrence of overfitting as much as possible. For the test data, the images of five different types of FMCG products were crawled by crawler, and then the steps of data cleaning, sample expansion, resolution conversion, labeling, and storing as test.mat and train.mat files were carried out sequentially. Finally, the fully connected convolutional neural network was built, the datasets were read into it, and the training effect was tested after training to get the FMCG recognition accuracy. After that, it was necessary to compare the actual accuracy with the expected accuracy. If the expected accuracy was not reached, adjusted the parameters in the fully connected convolutional neural network and repeated the training and testing process until the final test accuracy met the requirements. In summary, this research and design work on deep learning based FMCG recognition system embodied in this paper has been successfully completed. The features of this FMCG recognition system: light weight, easy to maintain and port; the recognition accuracy for a given FMCG picture is high, and the performance requirements for the running system are low.
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