espiownage：利用监控技术追踪钢鼓敲击过程中的瞬态现象
espiownage: Tracking Transients in Steelpan Drum Strikes Using Surveillance Technology
AI总结：
针对钢鼓ESPI高速视频特征追踪问题，采用最新计算机视觉库并清理数据集，引入鼓面分割-回归图与加速迭代工作流，将指标提升10%以上且18天完成项目。
摘要：
我们提出了一种改进方法，可提升对经电子散斑干涉法（ESPI）照明的加勒比钢鼓高速视频中特征进行有效追踪的能力。该方法通过采用最新的计算机视觉库实现目标检测与图像分割，同时投入大量工作清理此前用于训练该应用系统的数据集。除了将之前的指标得分提升10%及以上外，本项目的值得关注之处还包括：引入了适用于整个鼓面的分割-回归图，其生成的干涉条纹计数与通过目标检测得到的结果相当；此外还构建了加速工作流，用于协调数据清理与模型训练的反馈循环以实现快速迭代，使得本项目仅用18天就完成。
英文摘要：
We present an improvement in the ability to meaningfully track features in high speed videos of Caribbean steelpan drums illuminated by Electronic Speckle Pattern Interferometry (ESPI). This is achieved through the use of up-to-date computer vision libraries for object detection and image segmentation as well as a significant effort toward cleaning the dataset previously used to train systems for this application. Besides improvements on previous metric scores by 10% or more, noteworthy in this project are the introduction of a segmentation-regression map for the entire drum surface yielding interference fringe counts comparable to those obtained via object detection, as well as the accelerated workflow for coordinating the data-cleaning-and-model-training feedback loop for rapid iteration allowing this project to be conducted on a timescale of only 18 days.
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