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Research on Coal Gangue Identification by Using Convolutional Neural Network

机译：基于卷积神经网络的煤Gang石识别研究

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摘要

A237 at the problems of traditional coal gangue image recognition methods such as difficult extraction of artificial features and low accuracy of recognition, a method of automatic identification of coal gangue images by convolution neural network is proposed. Based on the classic convolution neural network LeNet-5, this method is improved from input sample size, activation function, network depth, size and number of convolution kernels, classification function and so on. The learning curve of the improved network shows its structure is reasonable. The recognition rate of the original LeNet-5 is only 50.67%. After the improvement, the recognition rate of coal gangue reaches 95.88%, which is much higher than that of the traditional recognition method. The results show that convolution neural network can effectively learn and extract the image features of coal and gangue automatically, classify coal and gangue, and provide reference for identification and classification of coal and gangue.
机译：A237针对传统煤image石图像识别方法人工特征提取困难，识别精度低的问题，提出了一种利用卷积神经网络自动识别煤gang石图像的方法。该方法基于经典的卷积神经网络LeNet-5，从输入样本大小，激活函数，网络深度，卷积核的大小和数量，分类函数等方面进行了改进。改进后的网络的学习曲线表明其结构是合理的。原始LeNet-5的识别率仅为50.67 \％。改进后，煤石的识别率达到了95.88％，远高于传统的识别方法。结果表明，卷积神经网络可以有效地自动学习和提取煤and石的图像特征，对煤and石进行分类，为煤and石的识别和分类提供参考。

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