Supermarket Commodity Recognition Method Based on Deep Learning

Abstract

The present invention proposes a deep learning-based supermarket product target recognition method, which solves the problem of low recognition rate in real supermarket scenes in the prior art. The implementation plan is: 1) Make a supermarket shelf product training set; 2) Build a product detection network and perform training on the produced training set; 3) Input the shelf picture into the trained network model to get all the product targets in the picture 4) Use the output of the last convolutional layer in the network model as the feature of the commodity target; 5) Encode the commodity feature to obtain the commodity descriptor; 6) Calculate the commodity descriptor and the commodity model descriptor in the existing model library 7) Take the most similar product model as the recognition result. The invention can accurately detect the product target area in the shelf picture, and can correctly identify the product target, and can be used for supermarket shelf product management.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for recognizing a shelf commodity target, which can be applied to supermarket shelf commodity management. Background technique [0002] In supermarkets, merchants and consumers need to obtain information about commodities on the shelves in real time. At present, the relevant information of these commodities is obtained manually, but the number of commodities in the supermarket is huge, and the way of manually obtaining commodity information is high in cost and low in efficiency. Therefore, the method of commodity recognition based on vision has important research significance and commercial value. [0003] The core of supermarket commodity recognition is target detection and recognition. Shaoqing Ren et al. published the paper "Faster R-CNN: Towards Real-Time Object Detection with Region ProposalNetworks" (IEEE Transactions on Pattern Analysis&Machin...

AI Technical Summary

Problems solved by technology

However, this method still cannot be applied to the identification of supermarket shelf products. The reasons are: 1. When this method performs ROI pooling on the feature vector of the candidate region, it will cause more target information loss. When the shelf product size is small, The recognition accuracy will be reduced; 2. This method is based on statistical recognition methods. There will often be new products in the supermarket and old products will be eliminated. Therefore, the classifier needs to be continuously retrained, which cannot meet the needs of real scenarios.

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