Abstract: In order to prevent or avoid the occurrence of tax evasion and taxation caused by non-invoicing of mineral resources such as coal,sand and gravel,it is an effective way to use the deep convolutional neural network to automatically identify empty vehicles.Based on the AlexNet model,this paper proposes5 kinds of improvement ideas for the difference of empty car and heavy vehicle images,and finally obtains a structure of 6-layer convolutional neural network based on maxout+dropout.The test results of the picture of the 34 220 empty cars and loaded cars show that the model has achieved good results in terms of accuracy,sensitivity,specificity and precision.In addition,the model is highly robust and can successfully identify a large number of empty car images with different angles and different scenes.

Key words: AlexNet, CNN, Deep learning, Empty car and loaded car identification, maxout