GOG-RT-DETR: An Improved RT-DETR-Based Method for Graphite Ore Grade Detection

To address the inefficiencies and inaccuracies of traditional ore grade identification methods in complex mining environments, and the challenge of balancing accuracy and speed on edge devices, this paper proposes a lightweight, high-precision, and high-speed detection model named GOG-RT-DETR. Built on the RT-DETR framework, the model incorporates a Faster-Rep-EMA module in the backbone network to reduce computational redundancy and enhance feature extraction. Additionally, a BiFPN-GLSA module replaces the CCFM module in the Neck network, improving feature fusion between the backbone and Neck networks, thus strengthening the model’s ability to capture both global and local spatial features. A Wise-Inner-Shape-IoU loss function is introduced to optimize the bounding box regression, accelerating convergence and improving localization accuracy. The model is evaluated on a custom-built graphite ore dataset with simulated data augmentation. Experimental results show that, compared to the baseline model, the mAP and FPS of GOG-RT-DETR are improved by 2.5% and 8.2%, with a 26.0% reduction in model parameters and a 23.37% reduction in FLOPs. This model enhances detection accuracy and reduces computational complexity, offering an efficient solution for ore grade detection in industrial applications.