Process refinement to consistently produce high-quality material over a large
area of the grown crystal, enabling various applications from optics crystals
to quantum detectors, has long been a goal for diamond growth. Machine learning
offers a promising path toward this goal, but faces challenges such as the
complexity of features within datasets, their time-dependency, and the volume
of data produced per growth run. Accurate spatial feature extraction from image
to image for real-time monitoring of diamond growth is crucial yet complicated
due to the low-volume and high feature complexity nature of the datasets. This
paper compares various traditional and machine learning-driven approaches for
feature extraction in the diamond growth domain, proposing a novel deep
learning-driven semantic segmentation approach to isolate and classify accurate
pixel masks of geometric features like diamond, pocket holder, and background,
along with their derivative features based on shape and size. Using an
annotation-focused human-in-the-loop software architecture for training
datasets, with modules for selective data labeling using active learning, data
augmentations, and model-assisted labeling, our approach achieves effective
annotation accuracy and drastically reduces labeling time and cost. Deep
learning algorithms prove highly efficient in accurately learning complex
representations from datasets with many features. Our top-performing model,
based on the DeeplabV3plus architecture, achieves outstanding accuracy in
classifying features of interest, with accuracies of 96.31% for pocket holder,
98.60% for diamond top, and 91.64% for diamond side features.

通过比较传统方法和机器学习驱动方法，该论文提出了一种新颖的深度学习驱动的语义分割方法，用于钻石生长领域的特征提取，实现了对钻石、挡板和背景等几何特征的像素掩膜的分离和分类，通过注重标注的人在环软件架构实现数据集的训练，结合主动学习、数据增强和模型辅助标注的模块，有效提高了标注的准确性并显著降低了标注的时间和成本，其中 DeeplabV3plus 架构的模型在准确分类感兴趣特征方面的性能优秀，对挡板的准确率达到 96.31%，对钻石顶部的准确率达到 98.60%，对钻石侧面的准确率达到 91.64%。

以人工智能引导的特征分割技术对单晶金刚石生长中的特征进行建模

AI-Guided Feature Segmentation Techniques to Model Features from Single  Crystal Diamond Growth

Automatic image annotation has been an important research topic in
facilitating large scale image management and retrieval. Existing methods focus
on learning image-tag correlation or correlation between tags to improve
annotation accuracy. However, most of these methods evaluate their performance
using top-k retrieval performance, where k is fixed. Although such setting
gives convenience for comparing different methods, it is not the natural way
that humans annotate images. The number of annotated tags should depend on
image contents. Inspired by the recent progress in machine translation and
image captioning, we propose a novel Recurrent Image Annotator (RIA) model that
forms image annotation task as a sequence generation problem so that RIA can
natively predict the proper length of tags according to image contents. We
evaluate the proposed model on various image annotation datasets. In addition
to comparing our model with existing methods using the conventional top-k
evaluation measures, we also provide our model as a high quality baseline for
the arbitrary length image tagging task. Moreover, the results of our
experiments show that the order of tags in training phase has a great impact on
the final annotation performance.

该研究提出了一种新的递归图像注释器（RIA）模型，将图像注释任务形成为序列生成问题，以便 RIA 可以根据图像内容本地预测正确的标记长度。 该模型在各种图像注释数据集上进行了评估，并显示其在任意长度图像标记任务上作为高质量基准的能力。 此外，实验结果表明，在训练阶段中标记的顺序对最终注释性能有很大影响。