Abstract
Deep learning has had a significant impact on the identification and classification of mineral resources, especially playing a key role in efficiently and accurately identifying different minerals, which is important for improving the efficiency and accuracy of mining. However, traditional ore sorting meth- ods often suffer from inefficiency and lack of accuracy, especially in complex mineral environments. To address these challenges, this study proposes a method called OreYOLO, which incorporates an attentional mechanism and a multi-scale feature fusion strategy, based on ore data from gold and sul- fide ores. By introducing the progressive feature pyramid structure into YOLOv5 and embedding the attention mechanism in the feature extraction module, the detection performance and accuracy of the model are greatly improved. In order to adapt to the diverse ore sorting scenarios and the deployment requirements of edge devices, the network structure is designed to be lightweight, which achieves a low number of parameters (3.458M) and computational complexity (6.3GFLOPs) while maintaining high accuracy (99.3% and 99.2%, respectively). In the experimental part, a target detection dataset containing 6000 images of gold and sulfuric iron ore is constructed for gold and sulfuric iron ore classification training, and several sets of comparison experiments are set up, including the YOLO series, EfficientDet, Faster-RCNN, and CenterNet, etc., and the experiments prove that OreYOLO outperforms the commonly used high-performance object detection of these architectures
Abstract (translated)
Deep学习在矿物识别和分类方面取得了显著影响，尤其是在高效准确地识别不同矿物方面发挥了关键作用，这对于提高采矿的效率和准确性至关重要。然而，传统的矿石分类方法通常存在效率低和准确性不足的问题，尤其是在复杂矿石环境中。为解决这些挑战，本研究提出了一个名为OreYOLO的方法，该方法基于黄金和硫铁矿的矿石数据，采用关注机制和多尺度特征融合策略。通过将逐步特征金字塔结构引入到YOLOv5中，并在特征提取模块中嵌入注意力机制，模型的检测性能和准确性得到了极大的提高。为了适应多样矿石分类场景和边缘设备的部署需求，网络结构被设计为轻量化，达到低参数（3.458M）和高计算复杂度（6.3GFLOPs），同时保持高准确率（99.3%和99.2%）。在实验部分，为构建黄金和硫铁矿矿石分类训练的目标检测数据集，建立了包含6000个图像的黄金和硫铁矿矿石分类训练数据集，并设置了几组比较实验，包括YOLO系列、EfficientDet、Faster-RCNN和CenterNet等，实验结果表明，OreYOLO在这些架构中使用的通常高性能物体检测方法中表现优异。
URL
https://arxiv.org/abs/2405.02785
