RFID Tag Classification for False-Positive Reads Identification via Deep Learning With Multidimensional Signal Fusion

Abstract

Radio-frequency identification has been widely used in logistics, significantly improving logistics management’s informatization level and efficiency. However, false-positive detection is discussed as one of the major results leading to the recording error. Particularly in industrial scenarios, the measurement noise and high accuracy requirements pose challenges to the tag classification methods. In this paper, a novel two-stage deep learning model consisting of a multi-dimension deep residual network for feature learning and a single-dimensional convolutional neural network for feature fusion and classification is proposed. In the first stage, the multi-dimension deep residual networks are trained in each signal dimension respectively, to extract the optimal single-dimensional features for classification. In the second stage, the single-dimensional convolutional neural network is used for feature fusion and deeper feature learning to obtain a robust classifier. Real data obtained from a laboratory warehouse environment and a real logistics workshop in an automobile factory respectively are used to evaluate the performance of the proposed model. Experimental results show that the proposed method has satisfactory accuracies both in the laboratory and real industrial environment and has portability, specifically, which provides the classification accuracy of 100% in the laboratory and higher than 99.91% in the real industrial environment.