  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
