| Autor: |
Marc Levesque, Virginia Foot, Simon Turbide, Henrique Weber, Camilla Robinson, Louis St-Laurent, François Babin |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXI. |
| DOI: |
10.1117/12.2560078 |
| Popis: |
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 0.5m2/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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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