计算机科学 ›› 2022, Vol. 49 ›› Issue (4): 227-232.doi: 10.11896/jsjkx.210300193
高心悦1, 田汉民1,2
GAO Xin-yue1, TIAN Han-min1,2
摘要: 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。
中图分类号:
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