Improving Graphite Ore Grade Identification with a Novel FRCNN-PGR Method Based on Deep Learning

Abstract

Graphite stone is widely used in various industries, including the refractory, battery making, steel making, expanded graphite, brake pads, casting coatings, and lubricants industries. In the mineral processing industry, an effective and accurate diagnostic method based on FRCNN-PGR is proposed and evaluated, which involves cutting images to expand the dataset, combining them using the faster R-CNN model with high and low feature layers, and adding a global attention mechanism, Relation-Aware Global Attention Network (RGA), to extract features of interest from both the space and channel. The proposed model outperforms the original faster R-CNN model with 80.21% mAP and 87.61% recall on the split graphite mine dataset.

Keywords

• deep learning
• faster R-CNN
• graphite grade
• multi-scale fusion
• relation-aware global attention
• filter response normalization (FRN)