论文标题
使用深钢筋学习对去角质二维晶体的动态缩放检测
Dynamic zoom-in detection of exfoliated two-dimensional crystals using deep reinforcement learning
论文作者
论文摘要
由于其可调节性和多功能性，二维材料是进行各种实验的绝佳平台。但是，艰苦的装置制造程序仍然是主要的实验挑战。一个瓶颈正在从大量的剥落晶体中搜索小靶晶体，这些晶体的形状和尺寸差异很大。我们提出了一种基于深钢筋学习和对象检测的组合，以准确有效地从包含许多微叶片的高分辨率图像中发现目标晶体。所提出的方法会动态放大感兴趣的区域，并用精细的检测器对其进行检查。可以定制我们的方法，用于搜索具有适度计算功率的各种晶体。我们表明，我们的方法在检测任务中的表现优于简单的基线。最后，我们分析了深钢筋学习剂在搜索晶体中的效率。代码可在\ url {https://github.com/stephandkim/detect_crystals}中获得。
Owing to their tunability and versatility, the two-dimensional materials are an excellent platform to conduct a variety of experiments. However, laborious device fabrication procedures remain as a major experimental challenge. One bottleneck is searching small target crystals from a large number of exfoliated crystals that greatly vary in shapes and sizes. We present a method, based on a combination of deep reinforcement learning and object detection, to accurately and efficiently discover target crystals from a high resolution image containing many microflakes. The proposed method dynamically zooms in to the region of interest and inspects it with a fine detector. Our method can be customized for searching various types of crystals with a modest computation power. We show that our method outperformed a simple baseline in detection tasks. Finally, we analyze the efficiency of the deep reinforcement learning agent in searching crystals. Codes are available at \url{https://github.com/stephandkim/detect_crystals}.
