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To speed up the comprehensive utilization and treatment of copper tailings, the digital image processing technology is proposed in this study to detect the low-silicon copper tailings (LSCT) using a scanning electron microscope (SEM), and the particle size distribution (PSD) and the activity of LSCT are analysed under the action of mechanical force. Firstly, the current status and application of copper tailings are introduced, and the influence of the particle size of LSCT on its practical application performance is explained. Secondly, the LSCT SEM image target recognition model is designed based on the convolutional neural network (CNN), and the model parameters and the reference CNN are selected. Finally, the experimental process is designed, a SEM image data set of LSCT is prepared, the model is trained through the training set, and the image recognition test is performed on the produced data set. The experimental results show that when the number of iterations of the CNN is 10, the accuracy of model recognition can be guaranteed. After the action of mechanical force, the PSD of LSCT is mainly concentrated at 1 μm~100 μm; that around 1.4 μm~10 μm is the largest, and the PSD of LSCT around 1.4 μm increases with the increase of action time of mechanical force, but the PSD of the LSCT begins to increase when the grinding time exceeds 150 minutes, and the activity of LSCT reaches the maximum (75.545%) at 150 minutes. The average accuracy of SEM image detection of the model is 86.97%, and the model based on DenseNet shows better recognition accuracy than other models. This study provides a reference for analysing the PSD of LSCT.

Image Analysis Technology in the Detection of Particle Size Distribution and the Activity Effect of Low-Silicon Copper Tailings