Encoding recognition for core fuel assembly based on improved LeNet-5 model
In the underwater assembly of core fuel assemblies for nuclear power plants,visual technology is used to identify component codes to accurately locate the installation position of the nuclear fuel assemblies.In response to the problem of reduced image quality caused by weak lighting in underwater environments,the underwater image enhancement is achieved by means of multiplicative enhancement algorithm,OSTU algorithm,CLAHE algorithm and Laplace transform.To improve the performance of encoding recognition,a model that integrates LeNet-5 network and support vector machine(SVM)is proposed in this paper.BN(Batch Normalization)layer and Dropout layer are added to the network to accelerate the running speed of the network,and the Sigmoid function is improved to increase the smoothness of the function to reduce the gradient vanishing.Experiments show that the validation accuracy on the customized dataset is 99.82％and the recognition rate is 100％,which is a significant improvement compared to other models.
NETL
NSTL
万方数据
维普
