@ARTICLE{Cai_Gaipin_Ore_2025,
 author={Cai, Gaipin and Luo, Hui and Huang, Jinruo and Yi, Guotao},
 volume={vol. 41},
 number={No 2},
 journal={Gospodarka Surowcami Mineralnymi - Mineral Resources Management},
 pages={177–196},
 howpublished={online},
 year={2025},
 publisher={Komitet Zrównoważonej Gospodarki Surowcami Mineralnymi PAN},
 publisher={Instytut Gospodarki Surowcami Mineralnymi i Energią PAN},
 abstract={The existing target detection algorithms detect the ore on the conveyor belt after the crushing process with low precision and slow detection speed. This leads to challenges in achieving a balance between precision and speed, to enhance the detection precision and speed of ore, and in view of the problems of leakage, misdetection, and insufficient feature extraction of YOLOv5 in the task of ore image detection; this study presents a target detection approach relying on the CA attention mechanism (Coordinate attention for efficient mobile network design), the SIoU loss function and the target detection algorithm YOLOv5 combination of ore image particle target detection method. Integrating the CA attention mechanism into the YOLOv5 backbone feature network enhances the feature learning and extraction of ore images, thereby improving the precision of the detection model; the SIoU loss function is refined to boost the recognition precision of the network on ore images and address the shortcomings of the original loss function that fails to take angular loss, distance loss, and shape loss into account, thereby further improving the precision and speed of ore image detection. The experimental findings demonstrate that the AP value, value, and precision rate are improved compared with the pre-improved algorithm. The CA-YOLOv5 method is verified to be fast, effective, and advanced and provides a foundation for real-time target detection of ores on conveyor belts in subsequent intelligent mine production.},
 type={Article},
 title={Ore image target detection based on improved YOLOv5 network},
 URL={http://ochroma.man.poznan.pl/Content/135594/177%E2%80%93196_Luo%20i%20inni.pdf},
 doi={10.24425/gsm.2025.154546},
 keywords={YOLOv5, object detection, coordinate attention for efficient mobile network design, feature extraction},
}