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首页> 外文期刊>Arabian journal of geosciences >Development of machine vision-based ore classification model using support vector machine (SVM) algorithm
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Development of machine vision-based ore classification model using support vector machine (SVM) algorithm

机译：基于机器视觉的矿石分类模型的开发使用支持向量机（SVM）算法

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摘要

The product of the mining industry (ore) is considered to be the raw material for the metal industry. The destination policy of the raw materials of iron mine is highly dependent on the class of iron ores. Thus, regular monitoring of iron ore class is the urgent need at the mine for accurately assigning the destination policy of raw materials. In most of the iron ore mines, decisions on ore class are made based on either visual inspection by the geologist or laboratory analyses of the ores. This process of ore class estimation is time consuming and also challenging for continuous monitoring. Thus, the present study attempts to develop an online vision-based technology for classification of iron ores. A laboratory-scale transportation system is designed using conveyor belt for online image acquisition. A multiclass support vector machine (SVM) model was developed to classify the iron ores. A total of 2200 images were captured for developing the ore classification model. A set of 18 features (9-histogram-based colour features in red, green and blue (RGB) colour space and 9-texture features based on intensity (I) component of hue, saturation and intensity (HSI) colour space) were extracted from each image. The performance of the SVM model was evaluated using four confusion matrix parameters (sensitivity, accuracy, misclassification and specificity). The SVM model performance was also compared with the other methods like K-nearest neighbour, classification discriminant, Naive Bayes, classification tree and probabilistic neural network. It was observed that the SVM classification model performs better than the other classification methods.
机译：采矿业（O​​re）的产品被认为是金属工业的原料。铁矿原料的目的地政策高度依赖于铁矿石类。因此，定期监测铁矿石课是矿井的迫切需要，以准确分配原材料的目的地政策。在大多数铁矿石矿山中，矿石课程的决定是基于地质学家或实验室分析的视野检查。矿石类估计的这个过程是耗时，并且对连续监测的挑战也是具有挑战性的。因此，本研究试图开发基于在线视觉的铁矿石分类技术。使用用于在线图像采集的传送带设计了实验室级运输系统。开发了一种多牌支持向量机（SVM）模型以分类铁矿石。捕获共2200个图像以开发矿石分类模型。提取了一组18个特征（基于红色，绿色和蓝色（RGB）颜色空间和基于色调的强度（i）组分的红色，绿色和蓝色（RGB）颜色空间和9个纹理特征的一组特征，提取了色调，饱和度和强度（HSI）颜色空间的强度（i）分量）来自每个图像。使用四个混淆矩阵参数（灵敏度，准确性，错误分类和特异性）评估SVM模型的性能。 SVM模型性能也与K-Collect邻居，分类判别，幼稚贝叶斯，分类树和概率神经网络等其他方法进行了比较。观察到SVM分类模型比其他分类方法更好。

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