焦炭显微光学组织自动检测与提取方法研究

Research on automatic detection and extraction of coke optical texture

焦炭显微光学组织结构测定是一种重要的焦炭质量评价方式,针对焦炭显微图像边缘模糊对比度低且存在光晕伪影等问题,设计一种基于语义分割和全连接条件随机场的焦炭显微光学组织自动检测与提取方法。首先,利用偏光显微镜、工业相机和上位机等搭建焦炭显微光学组织测定平台;其次,利用残差模块和注意力模块改进Unet网络模型,加强显微光学组织区域的输出权重,实现对焦炭光学组织的自动检测与分割;最后,使用全连接条件随机场对显微光学组织的空间特性进行建模,细化分割边缘,精确提取焦炭显微光学组织。实验结果表明,所提方法的精确度、召回率、F1分数和准确率分别达到了0.967、0.959、0.963、0.965,优于其他对比语义分割网络,证明该方法具有较高的分割性能,能够实现对焦炭显微光学组织的自动检测与提取。

Coke optical texture analysis is an important way to evaluate the quality of coke,aiming at the problems of fuzzy edge,low contrast and halo artifacts in coke photomicrographs,an automatic detection and extraction method for coke optical texture based on semantic segmentation and fully connected conditional random field is designed.Firstly,a coke optical texture measurement platform is built by using microscope,industrial camera and computer;secondly,the Unet is improved using residual module and attention module and the output weight of the coke optical texture is enhanced to realize automatic detection and segmentation of coke optical texture;finally,the spatial characteristics of coke optical texture are modeled using the fully connected conditional random field to refine the segmentation edges and achieve the accurate extraction of coke optical texture.The experimental results show that the precision,recall,F1-score and accuracy of the proposed method reach 0.967,0.959,0.963 and 0.965,respectively,which are better than other comparative semantic segmentation networks,proving that the method has high segmentation performance and can realize automatic detection and extraction of coke optical texture.