DropletMask: Leveraging visual data for droplet impact analysis  

作　　者：Chuanning Zhao Youngjoon Suh Yoonjin Won 

机构地区：[1]Department of Mechanical and Aerospace Engineering,University of California,Irvine,Irvine,California,USA [2]Department of Electrical Engineering and Computer Science,University of California,Irvine,Irvine,California,USA

出　　处：《Droplet》2024年第4期10-20,共11页液滴（英文）

基　　金：the support from the National Science Foundation(award number 2045322).

摘　　要：Machine learning-assisted computer vision represents a state-of-the-art technique for extracting meaningful features from visual data autonomously.This approach facili-tates the quantitative analysis of images,enabling object detection and tracking.In this study,we utilize advanced computer vision to precisely identify droplet motions and quantify their impact forces with spatiotemporal resolution at the picoliter or millisecond scale.Droplets,captured by a high-speed camera,are denoised through neuromorphic image processing.These processed images are employed to train con-volutional neural networks,allowing the creation of segmented masks and bounding boxes around moving droplets.The trained networks further digitize time-varying multi-dimensional droplet features,such as droplet diameters,spreading and sliding motions,and corresponding impact forces.Our innovative method offers accurate measurement of small impact forces with a resolution of approximately 10 pico-newtons for droplets in the micrometer range across various configurations with the time resolution at hundreds of microseconds.

关 键 词：VISUAL IMPACT utilize 

分 类 号：O35[理学—流体力学]