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This article reports nonintuitive characteristic of a splashing drop on asolid surface discovered through extracting image features using a feedforwardneural network (FNN). Ethanol of area-equivalent radius about 1.29 mm wasdropped from impact heights ranging from 4 cm to 60 cm (splashing threshold 20cm) and impacted on a hydrophilic surface. The images captured when half of thedrop impacted the surface were labeled according to their outcome, splashing ornonsplashing, and were used to train an FNN. A classification accuracy higherthan 96% was achieved. To extract the image features identified by the FNN forclassification, the weight matrix of the trained FNN for identifying splashingdrops was visualized. Remarkably, the visualization showed that the trained FNNidentified the contour height of the main body of the impacting drop as animportant characteristic differentiating between splashing and nonsplashingdrops, which has not been reported in previous studies. This feature was foundthroughout the impact, even when one and three-quarters of the drop impactedthe surface. To confirm the importance of this image feature, the FNN wasretrained to classify using only the main body without checking for thepresence of ejected secondary droplets. The accuracy was still higher than 82%,confirming that the contour height is an important feature distinguishingsplashing from nonsplashing drops. Several aspects of drop impact are analyzedand discussed with the aim of identifying the possible mechanism underlying thedifference in contour height between splashing and nonsplashing drops.

Image features of a splashing drop on a solid surface extracted using a feedforward neural network