AI 中文总结
研究微重力燃烧图像中液滴火焰直径测量难题，提出将自动提示点生成集成到Segment Anything Model 2的工作流程，经多液滴火焰图像验证，该流程精度高、效率提升显著，为火焰直径提取提供了自动化数字测量系统。
AI 中文摘要
火焰直径是微重力液滴燃烧中的关键可测量参数，但从自发光帧中提取它仍然很困难，因为烟灰尾、模糊的发光边界、腔室反射和液滴漂移会引入大量测量偏差和操作员依赖性。这项工作提出了一种基于人工智能的数字测量工作流程，用于从燃烧图像中自动测量火焰直径。该工作流程将自动提示点生成集成到Segment Anything Model 2中，采用基于随机样本一致性（RANSAC）的圆拟合。自动提示策略消除了主观手动选点，视频内存机制保持了漂移液滴的时间一致性，RANSAC拟合将烟灰尾像素作为几何异常值剔除。该方法通过19537张具有不同初始直径的正庚烷、正癸烷和正辛烷液滴的火焰图像进行了验证。与手动参考测量相比，该工作流程实现了96.9%的平均相对一致性、3.1%的平均绝对百分比误差，并且明显优于传统的霍夫圆检测，在相同评估条件下传统方法表现更差。结果还表明，测量精度随着液滴尺寸的增加而提高。所提出的工作流程具有8.54%的组合标准不确定度，并且在效率上比手动测量提高了约229倍。这些结果表明，所提出的基于SAM2的工作流程提供了一个可重复、完全自动化且具有计量学特征的数字测量系统，用于从具有挑战性的燃烧图像中提取火焰直径。该方法支持高通量燃烧诊断，并表明基于人工智能的分割可以集成到基于图像的数字化计量的定量测量工作流程中。
英文摘要
Flame diameter is a key measurable parameter in microgravity droplet combustion, but its extraction from self-illuminated frames remains difficult because soot tails, blurred luminous boundaries, chamber reflections, and droplet drift introduce substantial measurement bias and operator dependence. This work presents an AI-enabled digital measurement workflow for automated flame diameter from combustion images. The workflow integrates automatic prompt-point generation into Segment Anything Model 2, employing Random Sample Consensus (RANSAC)-based circle fitting. The automatic prompt strategy removes subjective manual point selection, while the video memory mechanism maintains temporal consistency for drifting droplets, and the RANSAC fitting rejects soot-tail pixels as geometric outliers. The method is validated by 19,537 flame images of n-heptane, n-decane, and n-octane droplets with varying initial diameters. Compared with manual-reference measurements, the proposed workflow achieves a mean relative agreement of 96.9%, a mean absolute percentage error of 3.1%, and substantially outperforms conventional Hough circle detection, which performed worse under the same evaluation conditions. The results also show that the measurement accuracy improves with increasing droplet size. The proposed workflow has a combined standard uncertainty of 8.54% and achieves approximately a 229-fold improvement in efficiency over manual measurement. These results demonstrate that the proposed SAM2-based workflow provides a reproducible, fully automated, and metrologically characterized digital measurement system for extracting flame diameter from challenging combustion images. The approach supports high-throughput combustion diagnostics and illustrates that AI-based segmentation can be integrated into quantitative measurement workflows for digitalized image-based metrology.
