A pragmatic and reproducible framework for real-time ore sorting in open-pit mining using a lightweight edge-deployed YOLO model

Zairov S. Umirzokov A. Abdufattokhov S. Vladimir D. Akhmatnurov D. Eshonkulov U. Zamaliyev N. Mekhmonov M. Gulmurodov J. Ravil M.
December 2026 Springer Nature

Journal of Engineering and Applied Science
2026 #73 Issue 1

Pre-sorting ore from waste material at the excavation face plays a key role in improving operational efficiency in open-pit mining operations. Automated solutions for this purpose need to be robust, operate in real time, and function on hardware with limited resources. This paper introduces a practical and reproducible framework for creating a lightweight ore classification system that can be deployed at the edge. The approach draws on a pre-trained YOLOv5s architecture, adapted via transfer learning to suit the industrial task using a public rock classification dataset that has been reformulated strategically. In fact, the model emerged from an accessible workflow that emphasizes efficiency. It underwent rigorous evaluation on an unseen test set, where it attained an overall accuracy of 80.7% and an AUC of 0.895. Notably, the model showed a recall of 88.4% for the economically important Ore class, which supports the main goal in industry of reducing product loss. Analysis of the results indicated that the model remains well calibrated. Classification errors tended to cluster around a handful of rock types that appear visually similar, which points to straightforward ways to refine it further. The main value of this work lies not in a new architecture but in a full end-to-end blueprint. This blueprint demonstrates the practicality of tailoring advanced object detectors to actual mining needs. Evidence from the study suggests that an optimized YOLO model offers a reliable and efficient option for automated quality control right at the mining face.

Edge computing , Lightweight model , Object detection , Open-Pit mining , Ore sorting , Reproducible workflow , Transfer learning , YOLO

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Mining Department, Branch of the National Research Technological University MISIS in Almalyk, Almalyk, Uzbekistan
Department of Mining work, Tashkent State Technical University named after Islam Karimov, Tashkent, Uzbekistan
Automatic Control and Computer Engineering Department, Turin Polytechnic University in Tashkent, Tashkent, Kazakhstan
Department of Information Technologies, Tashkent International University of Education, Tashkent, Uzbekistan
Department of Mineral Deposit Development, NJSC Karaganda Technical University named after Abylkas Saginov, Karaganda, Kazakhstan
NJSC Karaganda Technical University named after Abylkas Saginov, Karaganda, Kazakhstan
Department of Geology and Mining, Karshi State Technical University, Karshi, Uzbekistan
Department of Mining work, Navoi State University of Mining and Technologies, Navoi, Uzbekistan

Mining Department
Department of Mining work
Automatic Control and Computer Engineering Department
Department of Information Technologies
Department of Mineral Deposit Development
NJSC Karaganda Technical University named after Abylkas Saginov
Department of Geology and Mining
Department of Mining work

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