摘要
针对混合颗粒的分类问题,传统算法多利用颗粒的二值化图像提取其特征,并通过精细的特征设计结合BP神经网络、支持向量机(SVM)等分类器进行分类,但颗粒粘连以及不精确的特征设计都会严重影响分类的准确率。利用卷积神经网络提取颗粒的特征,通过区域建议网络(RPN)搜索颗粒的位置,同时建立分类器,并结合全卷积网络实现像素级的颗粒分割。对由球形、长条形及非规则形颗粒组成的混合流动颗粒体系进行实验研究,结果表明:利用人工特征设计的SVM法可以达到87%的分类精确率和召回率,而基于卷积神经网络的方法则可以达到97%的分类精确率和93%的召回率,并且对于非规则颗粒的数目中位径,该方法不仅可以将分析误差降低11%以上,还避免了传统方法需要精确设计人工特征等的不足,更易形成一个端对端的混合颗粒分类体系,为流动混合颗粒的图像在线分析提供了更加有效的思路。
Traditional methods for mixed-particle classification usually extract particle features from binary images. After designing appropriate features according to the particle type, particles can be classified using widely known classifiers, such as back-propagation neural network and support vector machine(SVM). However, classifying touching particles is a challenging, and inappropriate feature design may further reduce the classification accuracy. Herein, a convolutional neural network(CNN) is utilized to extract the features for building mixed-particle image classifiers. In particular, particle locations in an image are determined using a region proposal network. Furthermore, a classifier is designed and combined with a fully convolutional network to achieve pixel-level particle segmentation. Experimental analysis is performed on some flowing-mixed-particle systems comprising spherical, elongated, and irregular particles. According to the analysis results, SVM method using manually designed features can achieve an average precision of 87% and recall of 87%, whereas those of the CNN-based method are up to 97% and 93%, respectively. The latter method can also reduce the analysis error by more than 11% for number median diameter(Dn50) of irregular particles. In addition, several shortcomings in traditional methods, such as the need for manually designed features are solved, making it easier to build an end-to-end system for effective real-time image analysis of flowing mixed particles.
作者
蔡杨
苏明旭
蔡小舒
Cai Yang;Su Mingxu;Cai Xiaoshu(School of Energy and Power Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2019年第7期115-124,共10页
Acta Optica Sinica
基金
国家自然科学基金(51776129)
关键词
测量
颗粒分类
卷积神经网络
支持向量机
measurement
particle classification
convolutional neural network
support vector machine
