Droplet Segmentation Method Based on Improved U-Net Network

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.