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Ensemble Learning for Retail Product Recognition with a Large Number of Classes

Under the recent trend of unmanned economy, the retail stores have reduced the manpower for service and cashier gradually. The retail product recognition becomes one essential problem for unmanned shopping. Although the success of deep neural network makes the object recognition feasible in various applications, it is still difficult to perform well on a large number of classes. This paper presents an ensemble learning approach to deal with recognition for the growing number of retail products. In the proposed technique, the object classification networks are first improved with feature extraction and block attention. The ensemble model is then constructed by integrating the multiple network models with the loss selection as model weights. In the experiments, the feasibility of our ensemble recognition method is validated with a number of production items. The results have demonstrated the effectiveness compared to the state-of-the-art recognition algorithms.