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Biosensing across wide areas using LED induced fluorescence

机译:使用LED诱导的荧光在大范围内进行生物传感

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A standoff biothreat detection and identification system for scanning large areas was designed, built and tested. The sensor is based on two wavelength ultraviolet light induced fluorescence (UVLIF) measured from a distance. The concept calls for multiple sensor modalities, fused to give the required overall performance. It makes use of multiple cameras, ambient light reflectance, high optical power and wavelength modulated UV LED illumination and synchronized fluorescence detection. A two-step operational mode is described along with results from independent demonstrations for each step. The first step is screening of the scene to recognize the surfaces that maximize the chances of biothreat detection and classification. This step used computer vision and artificial intelligence (semantic segmentation) for automation. The material constituting the surface is identified from color images. A second monochrome camera gives total "fluorescence" images excited with an intensity modulated 368nm UV illuminator. The second demonstration is scanning of slides (the "scene" in this case) from 1.2m away, threat detection (the spots on the slides) and classification via active multispectral fluorescence imaging at two different excitation wavelengths (280 and 368nm) and ambient light reflectance at up to 0.5m~2/min. It is primarily the surface characteristics that drive the difficulty of the detection and classification of biological warfare agents (BWAs) on surfaces, along with the amount of BWA present on the surface. This presentation details the results obtained, the lessons learned and the envisioned way ahead.
机译:设计,建造和测试了用于扫描大面积的对峙生物威胁检测和识别系统。该传感器基于从远处测量的两个波长的紫外线诱导荧光(UVLIF)。该概念要求采用多种传感器形式,并融合在一起以提供所需的整体性能。它利用了多个摄像头,环境光反射率,高光功率和波长调制的UV LED照明以及同步荧光检测。描述了两步操作模式,以及每个步骤的独立演示结果。第一步是对场景进行筛选,以识别能够最大程度地检测生物威胁和进行分类的机会的表面。此步骤使用计算机视觉和人工智能(语义分割)来实现自动化。从彩色图像识别构成表面的材料。第二台单色相机给出了用强度调制的368nm紫外线照明器激发的全部“荧光”图像。第二个演示是从1.2m远处扫描幻灯片(本例中为“场景”),威胁检测(幻灯片上的斑点)并通过在两个不同激发波长(280和368nm)和环境光下的主动多光谱荧光成像进行分类反射率高达0.5m〜2 / min主要是表面特征驱动了表面上生物战剂(BWA)的检测和分类以及表面上存在的BWA的数量的困难。本演示文稿详细介绍了获得的结果,经验教训和未来的设想。

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