# Improving Graphite Ore Grade Identification with a Novel FRCNN-PGR Method Based on Deep Learning
## 1 Introduction  
At present, rare earth elements, energy resources, and novel materials hold considerable value around the world. Graphite products, as well as a broad spectrum of downstream extensively processed commodities, are attracting growing attention and have become an essential, irreplaceable component in crucial sectors, such as national security, space exploration, and novel materials. However, conventional methods for identifying the grade of carbon content of graphite ore through chemical or physical tests lead to resource waste and low efficiency during the ore blending process. With the steady advancement and stability of computer hardware, the use of equipment, such as Graphics Processing Units (GPUs), has become widespread [1], and deep learning has made significant progress in target recognition and image classification [2]. [PERSON] et al. [3] employed deep learning techniques and drones placed at low altitudes to detect real-time vehicle speeds in the city, thus ensuring security for future unmanned smart cities. [PERSON] et al. [4] improved the Faster Region Convolutional Neural Network (Faster R-CNN) with feature splicing, hard negative mining, and multi-scale training, achieving notable results in face detection. Furthermore, [PERSON] et al. [5] introduced DenseNet-41 to the Faster R-CNN model, calculating deep features that yielded better application outcomes in handwritten digit recognition. [PERSON] and colleagues (2019) employed convolutional neural networks, a sophisticated class of deep learning algorithms, to discern rock types bearing resemblance to those indigenous to the red planet, Mars. This research endeavor was further supported by the effective deployment of a comprehensive and diverse dataset comprised of multiple Martian-like images [6]. Meanwhile, [PERSON] et al. (2020) utilized the improved YOLOv3 algorithm to enhance target detection accuracy [7]. In a scholarly investigation conducted by [PERSON] et al. (2020), a comparative analysis was undertaken between the utilization of YOLOv4 and Faster R-CNN models for the purpose of rock category detection. The outcome of the investigation revealed that the latter model exhibited greater accuracy in its detection performance. Furthermore, in a distinct research study [8], the aforementioned models were evaluated and contrasted based on their respective capabilities to discern objects within images, with results indicating that the Faster R-CNN model yielded more precise outcomes. [PERSON] et al. (2018) [9] have implemented the Mask R-CNN [10] architecture for the swift identification of vehicular damage in the aftermath of traffic accidents. A comprehensive investigation into the current state-of-the-art methods of object detection reveals the pervasive deployment of deep learning frameworks, albeit with varying feature extraction network processes. The foundational Faster R-CNN network model [11] has been subject to numerous modifications in its quest for more efficient performance, ranging from the utilization of lightweight models, such as MobileNet [12], to more compact ones, such as VGGNet [13] and ResNet [14]. Such adaptations incorporate the residual module, which plays a crucial role in generating feature maps with a single layer that possesses relatively lower resolution. To address the missed targets and the false detection of small targets and details after segmentation, this paper employs a multi-scale fusion approach to improve accuracy. Deep learning has gained significant attention from researchers across various fields for its ability to mimic human visual attention and its popularity in computer vision applications. Prior notable algorithms include Squeeze-and-Excitation Networks (SE-Net) [15], Selective Kernel Networks (SK-Net) [16], and Convolutional Block Attention Module (CBAM) [17]; of these, SE-Net is a channel-based network, SK-Net is a branch type network, and CBAM is a kind of hybrid method based on channel and spatial attention.