基于深度学习的墙式氧气吸入器检定方法OA北大核心CSTPCD

Aiming at the problems of large influence of manual reading and low verification efficiency of wall oxygen inhaler,a verification method of wall oxygen inhaler based on deep learning is proposed.Images of the wall oxygen inhaler are taken by an industrial camera and fed into the improved residual block ResNet-18 network for automatic readings of the float flow meter.Residual block structure improvement strategies include:add a dropout network layer in the direct connection path;remove the 1 × 1 convolutional layer in the residual block;use the LeakyReLU activation function instead of the ReLU activation function.The dataset is divided into training set and test set according to the ratio of 5∶1,and after 100 batches of training,the accuracy of the network model on the test set is 98.37％.The wall oxygen inhaler with qualified verification results of National Institute of Metrology is connected to the verification device for verification,and the maximum indication error of the float flowmeter is calculated to be±0.2 L/min,the error is within the allowable range,and the verification results are the same.The results show that this method can replace manual reading and effectively improve the verification efficiency.