关键词: TBM, 卷积神经网络, 岩渣分类, 出渣系统, 轻量化模型, AlexNet网络模型, VGG网络模型, Mobilenet 模型

Abstract: In order to predict the front geological conditions in real time during boring, the structural characteristics of traditional neural network model are analyzed based on the slag pictures of practical projects and an autonomous lightweight network model is designed. After training with slag pictures, the parameters of Mobilenet lightweight network model is adjusted with the data. The results show that: (1) With excess parameters and memory, the traditional neural network algorithm cannot deploy the edge terminal of slag recognition onsite, while the selfdesigned lightweight network can meet the accuracy requirements of onsite slag image classification. (2) The classification accuracy of Mobilenet lightweight network model reaches 97%, but its calculation amount is only 80%~90% of the amount of traditional neural network model.

Key words: TBM, convolutional neural network, rock slag classification, slagging system, lightweight model, AlexNet network model, VGG network model, Mobilenet model