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

翻译：文章中报道了在使用向前神经网络(FNN)提取图像特征后发现的固体表面上浮落的不直观特征。 约1. 29毫米的面积等值半径的乙醇从4厘米至60厘米的冲击高度( 悬浮临界值 20厘米) 下降, 并撞击了流水体表面。 当一半的下降影响表面时所捕捉的图像根据结果、 溅落或未冲动而被用于培训 FNN。 达到了96%以上的分类精确度。 为了提取FNN为分类而确定的图像特征, 所培训的FNNN为识别溅落下降的重量矩阵被视觉化。 值得注意的是, 视觉化显示, 受过训练的FNNN确定了撞击下方主体的等高, 以其重要特征区分了浮点和非冲压的浮力, 先前的研究没有报告过这种特征。 在整个撞击过程中, 即使跌落的1/4季度, 也发现了降压压下地平面的底平面, 将FNNUR的直值定位的直值标的直径标位置进行测量。