Abstract
Traditional ore identification methods usually analyze the physical or chemical properties of ore samples and get conclusions, but those methods are not efficient when faced with large quantities of samples. In this paper, the YOLOv4 object detection algorithm is introduced and trained as a classifier of 7 common ores, which can make classification and position prediction for the image of different kinds of ore samples. The model has a high recognition accuracy, and the loss function is easy to converge. Moreover, the model also adapted several data augmentation methods and utilize Auto Multi-Scale Retinex with Color Restoration (MSRCR) for reducing the influence photograph environment of the ore specimen. The paper also uses the random forest algorithm to enhance the adaptability of the model in complex environments. The model has the advantages of low cost, short detection time, and strong generalization ability, covering most application scenarios from raw ore to ore products.
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Wang, T. (2022). Ore Detection Method Based on YOLOv4. In: Jain, L.C., Kountchev, R., Tai, Y., Kountcheva, R. (eds) 3D Imaging—Multidimensional Signal Processing and Deep Learning. Smart Innovation, Systems and Technologies, vol 297. Springer, Singapore. https://doi.org/10.1007/978-981-19-2448-4_24
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DOI: https://doi.org/10.1007/978-981-19-2448-4_24
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