Abstract: In order to solve the problem of low accuracy of identification of complex types of concrete sand and gravel aggregates and realize the automatic identification of sand and gravel aggregate types,a CM-ResNet18 network model suitable for the identification of concrete sand and gravel aggregate types is proposed.Secondly,the ResNet18 model is selected as the backbone network,the CBAM module and the MHSA module are fused to enhance the model's ability to extract features,and then the Dropout function is added to improve the generalization performance of the neural network,and transfer learning is introduced into the training to accelerate the network convergence speed,and the last layer learning rate is increased to better adapt to the training data and improve the model performance.Experimental results show that the CM-ResNet18 model achieves an accuracy of up to 99.09% in the identification of raw materials.Compared with other network models AlexNet,VGG19,EfficientNet,ResNet18 and ResNet34,the CM-ResNet18 model has improved the recognition accuracy,precision,recall rate and F1-score,and the results show that the method has high practicability and feasibility in the identification of concrete sand and gravel aggregates.

Key words: Sand and gravel aggregate, Data augmentation, Residual network, Attention mechanisms, Transfer learning