A Non-Contact Method for Real-Time Stacked Sheets Counting with X-ray Absorption Spectra and Long Short-Term Memory Network

Abstract

Stacked sheets counting is an important segment in the printing and packaging industry. It can meet the strict quality control and avoid great economic loss. Traditional counting methods based on photoelectric sensors or image processing face the challenges of low efficiency, breakage, and low contrast. In this paper, a non-contact and real-time counting method was developed by combining broadband X-ray absorption spectra (XAS) with long short-term memory network (LSTM). First, 500 sheets of standard A4 (70 g/m 2 ) printing paper stacked one by one were scanned by the broadband XAS detection equipment. Second, the collected 500 broadband XAS data were pre-processed by principal component analysis (PCA) to reduce the data dimension. Finally, LSTM was constructed to extract the temporal features of XAS data and establish a relationship with the number of paper sheets; meanwhile, polynomial fitting model(PFM) and artificial neural network (ANN) were proposed to compare with LSTM. The results showed that the combination of broadband XAS and LSTM had a maximum error of 1.8504 sheets and a single measurement time of 0.006 sec. To the best of our knowledge, this work was the first study to analyze and utilize the broadband XAS and LSTM for counting task. It provided a new non-contact and real-time counting method for stacked sheets.