Microhole detection of glass ampoule based on improved GoogLeNet

Abstract:

In the field of glass ampoule packaging integrity detection，high voltage discharge method is commonly used to detect micron-level leaky hole defects. In view of the existing methods，it is difficult to find appropriate filtering mode，discrimination thresholds depend on manual design， and detection accuracy is low，a microhole detection method based on improved GoogLeNet is proposed. For the original discharge current data，through the wavelet transform (WT)，and using the generalized Morse wavelet function (GMW) as the basic wavelet，transform the one-dimensional current time series into a two-dimensional time-frequency index graph to present the complete details of the data. On the basis of GoogLeNet prototype， Relu activation function is introduced to reduce overfitting，the input convolution is reduced to 1 layer，and then Inception module cutting at three different levels is carried out. Comparative analysis shows that when only the first 6 Inception modules are used and the proportion of large-size convolution kernels is increased for Inception(4D)，the model can also achieve a better effect of microhole discrimination with fewer parameters.In the industrial computer of production site， the trained model was used to replace the original algorithm，and 1000 positive and negative samples were tested. The results show that the accuracy of the algorithm is 99.15%，and the positive sample missing rate is only 08%，which is better than the 96.45% accuracy rate and 5.3% missing rate of the existing method.