Methods for efficient identifying coal and gangue on mobile devices
ZHANG Yong
GUO Yongcun
CHEN Wei
WANG Shuang
CHENG Gang
Abstract：An efficient and improved mobile terminal coal and gangue recognition method(E-MobileNetv3)was proposed,aiming at the problems such as high complexity of traditional lightweight convolutional neural network model,slow recognition speed of mobile terminal,poor training and recognition effect on small sample data sets.The lightweight method of convolutional neural network model was analyzed,and the MobileNetv3 network was improved from the three aspects of attention mechanism,activation function and classification head.The spatial storage capacity,floating point operation number,inference time and recognition accuracy of the improved network before and after quantization were analyzed by the model quantization compression network deployment model on the mobile devices.The model was trained,deployed and tested in an experimental equipment for coal and gangue recognition at mobile device.The results show that the improved network model converges after 20 times of training,the convergence speed is fast,and the accuracy of training and verification is greater than 99%.After quantization and compression,the storage capacity of the improved model is smaller,only 24.64%of that of the original network,and the complexity of the model is greatly reduced.The inference time of mobile device is only 77 ms,and the recognition accuracy reaches 99.7%.The identification effect of coal and gangue images collected in real time by the experimental device is good,which verifies the reliability of the identification method.
Keywords：recognition of coal and ganguenetwork lightweightmodel compressionattention mechanismssmall datasetmobile device
Publication Date：2023-02-25
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：10( 61-70 )