基于改进U-Net网络的液滴分割方法

Droplet Segmentation Method Based on Improved U-Net Network

液滴图像的精确分割是高精度接触角测量的重要环节,针对在液滴分割过程中存在的目标不准确、轮廓不完整以及固-液-汽3项交点和边界细节效果不佳的问题,文中提出了一种适用于液滴分割的神经网络模型。该模型以U-Net网络为基础,在其输入处加入1×1卷积层汇总图像特征,避免从初始图像中丢失信息;并采用Resnet18结构作为U-Net的特征学习编码器,增强了网络的表达能力,促进了梯度的传播。在解码过程中引入密集连接的特征融合技术,在提升分割目标细节信息的同时降低了网络参数。最后在每个卷积层后都添加批量归一化操作,进一步优化了网络性能。实验结果表明,改进的U-Net模型能够有效提高液滴识别的准确率,提升分割效果,在接触角测量领域具有一定的参考价值。

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.