ZHANG Yong^a,c, GUO Yongcun^b,c, CHEN Wei^b,c, WANG Shuang^b,c, CHENG Gang^b,c

(a. School of Electrical and Information Engineering; b. School of Mechanical Engineering; c. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232001, China)

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 gangue; network lightweight; model compression; attention mechanisms; small dataset; mobile device

Received： 2022-05-22，Revised：2022-11-10，Online：2022-12-02 16:20。

Funding Project：国家自然科学基金项目，编号：51904007；安徽省科技重大专项资助项目，编号：18030901049；安徽省高校协同创新项目，编号：GXXT-2021-076。

First Author：张勇(1996—)，男，硕士研究生，研究方向为煤矸智能分选技术。E-mail：15955942745@163.com。

Corresponding Author：郭永存(1965—)，男，教授，博士，博士生、 硕士生导师，研究方向为煤矿智能化技术。E-mail： guoyc@aust.edu.cn。