Computer Science ›› 2022, Vol. 49 ›› Issue (4): 227-232.doi: 10.11896/jsjkx.210300193

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Droplet Segmentation Method Based on Improved U-Net Network

GAO Xin-yue1, TIAN Han-min1,2   

  1. 1 School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China;
    2 Tianjin Key Laboratory of Electronic Materials and Devices, Tianjin 300401, China
  • Received:2021-03-18 Revised:2021-04-09 Published:2022-04-01
  • About author:GAO Xin-yue,born in 1996,postgra-duate.Her main research interests include deep learning and picture processing.TIAN Han-min,born in 1975,Ph.D,professor.His main research interests include picture processing and contact angle measurement.

Abstract: The accurate segmentation of liquid drop image is an important part of high precision contact Angle measurement.Aiming at the problems of inaccurate target, incomplete contour, and poor effect of solid-liquid-vapor intersection and boundary details in the process of liquid drop segmentation, a neural network model suitable for liquid drop segmentation is proposed.The model is based on U-Net network, and a 1×1 convolution layer is added at its input to summarize image features to avoid losing information from the initial image.Resnet18 structure is used as the feature learning encoder of U-Net to enhance the expression ability of the network and promote the propagation of gradient.The feature fusion technology of dense connection is added in the decoding process, which improves the detail information of segmented target and reduces the network parameters.Finally, a batch normalization operation is added after each convolution layer to further optimize the network performance.Experimental results show that the improved U-Net model can effectively improve the accuracy of droplet identification and segmentation effect, and has a certain reference value in the field of contact Angle measurement.

Key words: Contact angle measurement, Droplet segmentation, Feature fusion, ResNet, U-Net

CLC Number: 

  • TP391
[1] ZHANG P.Study on contact angle measurement method based on image analysis technology[D].Shanghai:East China University of Science and Technology,2014.
[2] ZHAO K Y,TIAN H M,GUO D,et al.Influence of background light on high precision contact angle measurement[J].Instrument Technology and Sensors,2019(5):100-103.
[3] ZHANG T,TIAN H M,RONG X Y,et al.Application of particle swarm optimization canny operator in high precision contact angle measurement[J].Journal of Hebei University of Techno-logy,2018,47(3):30-35.
[4] WANG X H,LI J J,YANG W,et al.Image processing and detection of contact angle[J].Photoelectronic Technique,2011,31(1):14-19.
[5] ZHANG P,WAN Y Q,ZHOU Y L.A new boundary extraction algorithm and its application in contact angle measurement[J].Journal of East China University of Science and Technology(Natural Science),2014,40(6):746-751.
[6] ZHAO K Y,TIAN H M,GUO D,et al.Automatic measurement method of contact angle based on feature point detection[J].Journal of Electronic Measurement and Instrumentation,2018,32(11):147-153.
[7] ZHOU L L,JIANG F.A review of image segmentation methods[J].Computer Application Research,2017,34(7):1921-1928.
[8] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,39(4):640-651.
[9] RONNEBERGER O,FISCHER P,BROX T.U-Net:Convolu-tional networks for biomedical image segmentation [C]//International Conference on Medical Image Computing and Compu-ter-Assisted Intervention.Springer International Publishing,2015:234-241.
[10] DONG Y,FENG H J,XU Z M,et al.Attention Res-Unet:An efficient shadow detection algorithm[J].Journal of Zhejiang University (Engineering Science),2019,53(2):172-180,205.
[11] JING J,WANG Z,RÄTSCH M,et al.Mobile-Unet:An efficient convolutional neural network for fabric defect detection[J].Textile Research Journal,2020,66(5):1-17.
[12] WANG E K,CHEN C M,HASSAN M M,et al.A deep learning based medical image segmentation technique in internet-of-medical-things domain[J].Future Generation Computer Systems,2020,108:135-144.
[13] XIAO X,LIAN S,LUO Z,et al.Weighted res-unet for high-quality retina vessel segmentation[C]//2018 9th International Conference on Information Technology in Medicine and Education (ITME).IEEE Computer Society,2018.
[14] HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE Computer Society,2016:770-778.
[15] HUANG G,LIU Z,MAATEN L V D,et al.Densely connected convolutional networks[C]//IEEE Conference on Computer Visionand Pattern Recognition.2017:2261-2269.
[16] ROTHER C.GrabCut:Interactive foreground extraction using iterated graph cuts[J].Acm Trans Graph,2004,23(8):309-314.
[17] BADRINARAYANAN V,KENDALL A,CIPOLLA R.Seg-Net:A deep convolutional encoder-decoder architecture for image segmentation[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence.2017.
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