INTELLIGENT IDENTIFICATION METHOD OF STEEL MICROSTRUCTURE BASED ON DEEP LEARNING
Steel microstructure analysis is the process of analyzing the properties of steel materials according to the microstructure characteristics of steel materials.At present,the identification of micro-organization often relies on the judgment of professionals,which requires a lot of manpower and material resources,and is low in efficiency and easy to be affected by subjective factors,resulting in uncertain results.By improving the residual network model,an improved residual network model based on transfer learning is proposed.The ImageNet data set is pre-trained and the weights are transferred to the improved residual network model to realize deep learning under the small sample data set.The convolutional neural network model is tested on 16 kinds of steel material microstructure test sets,and the results show that the accuracy of the method is 95.36％,which is 6.9 percentage points higher than that of the basic network structure recognition rate.Compared with other network structure models,this model not only has high recognition rate but also strong generalization ability.
deep learningresidual networkweighttransfer learningmicrostructureintelligent recognition
万方数据
维普
