Graphite Ore Grade Classification Algorithm Based on Multi-scale Fused Image Features

Aiming at the problems of complex pre-processing and expensive equipment in chemical detection of graphite ore grade, a graphite ore identification and classification method based on fusing multi-scale image features is proposed. In the feature extraction stage, a deep convolutional neural network and a residual network model based on spatial attention mechanism are constructed to improve the learning ability of local and global features of graphite ore images; in the feature aggregation stage, a global response normalization technique is introduced to achieve more accurate graphite ore grade recognition, and the accuracy of the model reaches 93.401