论文标题
反馈数字显微镜中的实时定位和分类的卷积神经网络
Convolutional Neural Networks for Real-Time Localization and Classification in Feedback Digital Microscopy
论文作者
论文摘要
我们提出了一个适应于光学显微镜中颗粒的实时定位和分类的单发卷积神经网络（YOLOV2）。与以前的工作相比，我们专注于系统的实时检测功能，以允许在反馈控制的帮助下在大型异质集合中操纵微观对象。该网络也能够将数百个显微镜对象定位和分类，即使在非常低的信噪比之比，对于416x416像素，推理时间约为10 ms。我们通过操纵激光诱导的自疗推动的活性颗粒来证明实时检测性能。为了使我们的框架容易为他人提供，我们提供了所有脚本和源代码。该网络使用TensorFlow后端在Python/keras中实现。为实时推理提供了支持GPU的C库。
We present an adapted single-shot convolutional neural network (YOLOv2) for the real-time localization and classification of particles in optical microscopy. As compared to previous works, we focus on the real-time detection capabilities of the system to allow for manipulation of microscopic objects in large heterogeneous ensembles with the help of feedback control. The network is capable of localizing and classifying several hundreds of microscopic objects even at very low signal-to-noise ratios for images as large as 416x416 pixels with an inference time of about 10 ms. We demonstrate the real-time detection performance by manipulating active particles propelled by laser-induced self-thermophoresis. In order to make our framework readily available for others, we provide all scripts and source code. The network is implemented in Python/Keras using the TensorFlow backend. A C library supporting GPUs is provided for the real-time inference.
