论文标题
使用无透镜视频显微镜检测，跟踪和分类浮游生物的嵌入式系统
Embedded System to Detect, Track and Classify Plankton Using a Lensless Video Microscope
论文作者
论文摘要
浮游生物为地球上的生命提供了基础。为了促进我们对海洋生态系统的理解，出于科学，商业和生存目的，需要对浮游生物进行更多的持续监测和分析。成本，复杂性，功率和数据通信需求是广泛部署原位浮游生物显微镜的障碍。我们通过使用针对Raspberry Pi 3进行优化的数据管道来构建和表征无镜头显微镜来解决这些障碍。管道记录1080p在样品中游泳的多个浮游生物游泳的视频，同时检测，跟踪和选择明显的裁剪裁剪的图像，用于分类 @ 5.1帧每秒5.1帧。在九个浮游生物类别上评估了十三个机器学习分类器和九组功能组合的组合，对速度进行了优化（f1 = 0.74 @ 1毫秒。每个图像预测）和准确性（F1 = 0.81 @ .80 sec。）。系统性能结果证实，在低成本开源嵌入式计算机上可以执行从图像捕获到分类的整个数据管道。
Plankton provide the foundation for life on earth. To advance our understanding of the marine ecosystem, for scientific, commercial and survival purposes, more in situ continuous monitoring and analysis of plankton is required. Cost, complexity, power and data communication demands are barriers to widespread deployment of in situ plankton microscopes. We address these barriers by building and characterizing a lensless microscope with a data pipeline optimized for the Raspberry Pi 3. The pipeline records 1080p video of multiple plankton swimming in a sample well while simultaneously detecting, tracking and selecting salient cropped images for classification @ 5.1 frames per second. Thirteen machine learning classifiers and combinations of nine sets of features are evaluated on nine plankton classes, optimized for speed (F1=0.74 @ 1 msec. per image prediction) and accuracy (F1=0.81 @ .80 sec.). System performance results confirm that performing the entire data pipeline from image capture to classification is possible on a low-cost open-source embedded computer.
