Abstract: A high precision microporous plate turbidity classification algorithm based on convolutional neural network is proposed. This algorithm mainly combines the traditional image processing technology with the convolutional neural network technology. Through the traditional image processing algorithm, round holes are cut from the microporous plate images taken naturally, and the cut round hole images are made into round hole data sets for the training, evaluation and testing of network models. At the same time, through the deep learning technology, multiple convolutional neural network models based on the depth-separable convolution kernel are designed and trained. Then, the turbidity classification model with the highest evaluation accuracy is selected and applied to the circular hole identification system, thus improving the work efficiency of researchers.

Key words: image classification, deep learning, convolutional neural network