One-Shot Retail Product Identification Based on Improved Siamese Neural Networks

Conventional retail stores are undergoing digital transformation, and in a typical smart retail store, automatic recognition of retail products is essential for customer experience in the checkout stage. In this paper, we propose an improved Siamese neural network to identify the product from one-shot learning. First, a spatial channel dual attention mechanism is proposed to improve the network architecture. Second, a binary cross-entropy loss function with a distance penalty is adopted to replace the conventional contrastive loss function. The proposed network can better model the details of the products. The experimental results are achieved on two public available databases. The results show that the proposed method outperforms the conventional methods, and it can solve the data insufficient problem in the training stage. Smart retail stores can change the SKUs (Stock Keeping Units) conveniently without collecting a large amount of training samples.