Microscopic probes are used to visualise dispersions in fluid-fluid systems to characterize dispersion behaviors. An advanced multi-stage filtered Hough method and a deep learning based neural networking method (Faster R-CNN) has been compared for droplet detection performance on an image set. The comparison was made with respect to an independent manual analysis. A test analysis for droplet detection performance analysis was prepared. The Faster R-CNN method performed better relative to advanced filtered Hough method. The Faster R-CNN had within 12 $\%$ measurement error with respect to the visual or manual detection standard in terms droplet size measurement. This standardized method was utilized to analyze Exxsol D80 oil and water emulsions for evaluating droplet stability parameters. Accurate droplet stability coefficients in the inertial sub-range was evaluated alongside dependency on dispersion phase fraction. The droplet relaxation time scales for the Exxsol D80 oil and water system has been measured and reported where possible.

显微探针用于可视化流体-流体系统中的分散，以表征分散行为。已经比较了先进的多级滤波霍夫方法和基于深度学习的神经网络方法（Faster R-CNN）在图像集上的液滴检测性能。比较是针对独立的手动分析进行的。准备了液滴检测性能分析的测试分析。相对于高级过滤霍夫方法，Faster R-CNN 方法表现更好。Faster R-CNN 在 12$\%$在液滴尺寸测量方面，相对于视觉或手动检测标准的测量误差。这种标准化方法用于分析 Exxsol D80 油和水乳液，以评估液滴稳定性参数。惯性子范围内的精确液滴稳定性系数与对分散相分数的依赖性一起进行评估。Exxsol D80 油水系统的液滴弛豫时间尺度已在可能的情况下进行测量和报告。