用AI自动测微重力燃烧火焰直径，精度高且无需人工干预。
Digital measurement of droplet flame diameter in microgravity combustion images using Segment Anything Model 2 with automatic prompt selection
- 基于SAM2自动选点+RANSAC圆拟合，消除人为误差。
- 对19,537张图像测试，平均误差仅3.1%，精度达96.9%。
- 比传统方法快229倍，适合大规模燃烧实验分析。
火焰直径是微重力液滴燃烧中的关键可测量参数，但自发光图像中因烟尘尾迹、模糊光边界、腔体反射及液滴漂移，导致测量偏差大且依赖人工。本文提出一种基于SAM2的全自动火焰直径数字测量流程，集成自动提示点生成与随机采样一致性（RANSAC）圆拟合。自动提示策略消除主观选点，视频记忆机制保持漂移液滴的时间一致性，RANSAC剔除烟尘像素作为几何异常值。在19,537张正庚烷、正癸烷和正辛烷液滴燃烧图像上验证，相比人工基准，该方法平均相对一致性达96.9%，平均绝对百分比误差为3.1%，显著优于传统霍夫圆检测。结果表明，测量精度随液滴尺寸增大而提升。整体测量联合标准不确定度为8.54%，效率较人工提升约229倍。证明该SAM2工作流实现了可复现、全自动、量测可表征的数字化火焰直径提取系统，支持高通量燃烧诊断，展示AI分割可融入定量图像计量流程。
原文摘要 · Abstract (English)
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.
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