Research on 1D-DenseRNet-based classification and detection method for canned food vacu-um data
Aiming at the problem of low accuracy and high cost of vacuum detection in food cans,in order to realize the nondestructive detection of vacuum in food cans and improve the detection speed and accuracy,the research of measuring the curve data on the top of cans by using laser displacement sensors and categorizing the curve data by 1D-DenseRNet model is proposed.The model includes several improved dense connectivity modules,gate cycle u-nits,attention layers and residual connectivity modules to extract time-series voltage sequence features and capture long-term dependencies.A cross-validation approach was used to analyze the effect of different network layers on the model performance.The optimal network model structure is designed by changing the convolutional layer,combining the attention mechanism,different recurrent neural network modules and the residual network structure,and observ-ing the changes in other evaluation indexes such as model accuracy and model size.The experimental data show that the 1D-DenseRNet model incorporating the gate recurrent unit achieves the highest accuracy(98.77％)on the small sample dataset when the momentum factor is set to 0.9 and the learning rate is set to 0.000 5,and the number of model parameters is also relatively small.Comparison with a single convolutional neural network and other hybrid networks demonstrates the advantages of the 1D-DenseRNet model in handling the task of vacuum detection in food cans.
non-destructive testing of food cansneural networkvacuum detectionsmall sample data classification
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