Segmentation method of blast furnace radar burden lines based on BS-TransUNet network
Blast furnace radar burden line extraction currently commonly used neural network plus energy center of gravity method of two-step extraction of burden line method,there is a mixture of net-work model and mechanism model step-by-step computation,susceptible to the influence of the spe-cial environment of strong noise problem.In this paper,an improved BS-TransUNet algorithm for blast furnace burden line extraction based on semantic segmentation was proposed.Firstly,to address the problems of periodic morphology and particle size variation of blast furnace burden surface and signal-to-noise ratio attenuation,the atrous spatial pyramid pooling(ASPP)module is introduced between convolution neural network(CNN)and Transformer modules to obtain fine-grained features of the burden surface.Then,the coordinate attention(CA)module is integrated after each up-sampling to filter out the background noise more comprehensively and inhibit the extraction of ineffective high-fre-quency texture features.Finally,the jump link is replaced with the BiFusion module to further im-prove the segmentation performance.The experimental results show that the improved algorithm im-proves the mean intersection over union(MIoU)and F1 scores by 1.77％and 1.46％,respective-ly,the mean pixel accuracy(MPA)by 1.97％on the blast furnace radar burden surface dataset,and the F1 score can reach 86.18％.Compared to the conventional two-step extraction of burden line method,the one-step method with end-to-end split burden line in the harsh environment of a blast furnace provides improved accuracy and stability of burden line acquisition.
blast furnace radarburden line extractionsemantic segmentationBS-TransUNet
NETL
NSTL
万方数据
