OGI-RT-DETR: A Lightweight Vision-Sensing Model for Real-Time Graphite Ore Grade Recognition in Intelligent Sorting

Vision-based sensing is a practical approach for real-time ore grade recognition in intelligent mineral sorting. However, graphite ore images captured in industrial environments are often affected by illumination variation, background interference, surface oxidation, and subtle visual differences among grades, limiting the reliability of existing models on edge devices. To address these issues, this study proposes OGI-RT-DETR, a lightweight real-time end-to-end vision-sensing model for graphite ore grade recognition. Based on RT-DETR-r18, a PConv-Rep backbone enhancement module is designed to reduce redundant computation and improve the extraction of ore texture, edge, and irregular shape features. A CFPT feature fusion module is constructed to strengthen multi-scale semantic interaction and spatial fusion under complex backgrounds. A Wise-Focaler-MPDIoU bounding box regression loss is further proposed to improve localization stability and regression accuracy. Experiments were conducted on a self-built augmented graphite ore image dataset collected using an industrial camera system. The proposed OGI-RT-DETR achieved a Precision of 85.6%, Recall of 87.6%, mAP50 of 86.1%, and inference speed of 84.5 FPS. Compared with the baseline RT-DETR-r18, OGI-RT-DETR improved mAP50 by 3.7 percentage points and FPS by approximately 8.2%, while reducing FLOPs and parameters by 31.8% and 38.2%, respectively. These results indicate that OGI-RT-DETR can support accurate, efficient, and lightweight visual sensing for real-time graphite ore sorting.