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| An intelligent automatic analysis system of ore fragmentation in blasting based on deep learning |
| Received:June 01, 2022 Revised:June 10, 2022 |
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| DOI： |
| KeyWord:deep learning; MobileNet; U-Net; cloud platform; fragmentation measurement; fragmentation of ore; automatic analysis; intelligent blasting |
| Author | Institution |
| XU Wei |
北京矿冶研究总院 |
| DUAN Yun |
北京矿冶研究总院 |
| WANG Bonan |
北京矿冶研究总院 |
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| Abstract: |
| In the field of open-pit mining, the key to ingoptimizing the blasting design is to automatically and accurately obtain the ore fragmentation information of the blasting pile. because of the problem that the current fragmentation analysis system cannot automatically acquire and automatically process images in batches, this paper proposes an automatic fragmentation analysis system of ore based on deep learning. The system is mainly composed of an automatic acquisition subsystem based on the MobileNet classification model and an automatic analysis subsystem based on U-Net semantic segmentation model. The system takes pictures of an electric dump truck unloading at the crushing station automatically and continuously and uploads them to the cloud platform by 4G network for automatic analysis of fragmentation information. Evaluating the classification model and segmentation model qualitatively and quantitatively, the accuracy of the classification model on the test set reaches 98.08%, and the ore category IOU of the ore segmentation model reaches 78.43%. The system has been running at the cycle station in the open-pit mining area of a mine for more than a year. The results show that the system meets the design requirements, and realizes the automation and intellectualization of the whole process from collection to analysis and information display, which can further provide data support for intelligent blasting. |
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