Coal-gangue image classification method
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摘要: 针对人工排矸法、机械湿选法、γ射线分选法等传统煤矸石分选方法无法兼顾快速高效性、安全无害性、简单操作性的问题，提出了基于机器视觉的煤矸石图像分类方法。对煤矸石图像进行增强、平滑去噪等预处理，采用基于距离变换的分水岭算法实现煤矸石图像分割提取。针对煤矸石分割图像，选取煤矸石图像的HOG特征及灰度共生矩阵，分别以支持向量机、随机森林、K近邻算法作为分类器进行基于特征提取的煤矸石分类识别；分别建立浅层卷积神经网络和基于ImageNet数据集预训练的VGG16网络，进行基于卷积神经网络的煤矸石分类识别。研究结果表明，基于VGG16网络的煤矸石图像分类方法准确率最高为99.7%，高于基于特征提取方法的91.9%和基于浅层卷积神经网络方法的92.5%。Abstract: For problems that traditional coal-gangue separation methods such as manual separation method, mechanical wet-separation method, γ-ray separation method and so on could not give consideration to high efficiency, safety and easy operation, a coal-gangue image classification method based on machine vision was proposed. Coal-gangue image is pre-processed with enhancement, smoothing and denoising, then segmented and extracted by watershed algorithm based on distance conversion. HOG feature and gray-level co-occurrence matrix of the coal-gangue image are selected, and coal-gangue classification based on feature extraction is carried out by taking support vector machine, random forest and K-nearest neighbor algorithm as classifier separately. Coal-gangue image classification based on convolutional neural network is carried out by building shallow-level convolutional neural network and VGG16 network pre-trained by ImageNet dataset separately. The research results show that the maximum accuracy rate of the coal-gangue image classification method based on VGG16 is 99.7%, which is higher than that of the method based on feature extraction with 91.9% or the method based on shallow convolutional neural network with 92.5%.
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期刊类型引用(2)
1. 程林. 电子定量包装秤自动化二次校准系统设计. 现代电子技术. 2023(24): 34-38 . 
百度学术
2. 胡飞，陈彩蓉，葛飞，曹文韬，於海明. 生鲜乳快速冷却装置监测系统设计. 农业工程. 2022(09): 41-44 . 百度学术
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