Conventional analysis of droplet splashing — particularly in the small-droplet regime — has relied on visual inspection and manual measurement, limiting throughput, reproducibility, and objectivity. This project develops a deep-learning pipeline that automatically detects, classifies, and quantifies splashing morphologies and estimates splash boundaries with high precision from high-speed imaging. The framework will be benchmarked against the systematic experimental dataset built in the doctoral research, enabling objective, statistically robust comparison across a wide range of droplet temperatures and ambient pressures, and providing a transferable tool for the broader thermal-fluids community.

Conventional analysis of droplet splashing — particularly in the small-droplet regime — has relied on visual inspection and manual measurement, limiting throughput, reproducibility, and objectivity. This project develops a deep-learning pipeline that automatically detects, classifies, and quantifies splashing morphologies and estimates splash boundaries with high precision from high-speed imaging. The framework will be benchmarked against the systematic experimental dataset built in the doctoral research, enabling objective, statistically robust comparison across a wide range of droplet temperatures and ambient pressures, and providing a transferable tool for the broader thermal-fluids community.