Optical filter selection for high confidence discrimination of strongly overlapping infrared chemical spectra

Biomimetic Optical-Filter Sensor System for Discrimination of Infrared Chemical Signatures Against a Cold Sky Background

Passive infrared (IR) systems enable rapid detection of chemical vapors but are limited by size, weight, cost, and power. Previously, the authors reported a novel passive sensor that utilizes multiple IR filter/detector combinations to discriminate between different chemical vapors based on their unique IR absorption spectra in the same manner the human eye uses to generate colors. This approach enables a very small, compact, and low-power sensor system with the capability to discriminate between chemical vapors of interest and background chemicals. All previous work showed the capability of this sensor system in discriminating chemical vapors against a hot blackbody in a laboratory environment. Now the authors demonstrate the ability of this sensor system to discriminate between the chemical vapor agent simulant dimethyl methylphosphonate and ethanol against the cold sky in an outdoor environment.

Wide-Angular Tolerance Optical Filter Design and Its Application to Green Pepper Segmentation

The optical filter is critical in many applications requiring wide-angle imaging perception. However, the transmission curve of the typical optical filter will change at an oblique incident angle due to the optical path of the incident light change. In this study, we propose a wide-angular tolerance optical filter design method based on the transfer matrix method and automatic differentiation. A novel optical merit function is proposed for simultaneous optimization at normal and oblique incidents. The simulation results demonstrate that such a wide-angular tolerance design can realize a similar transmittance curve at an oblique incident angle compared to a normal incident angle. Furthermore, how much improvement in a wide-angular optical filter design for oblique incident contributes to image segmentation remains unclear. Therefore, we evaluate several transmittance curves along with the U-Net structure for green pepper segmentation. Although our proposed method is not perfectly equal to the target design, it can achieve an average 50% smaller mean absolute error (MAE) than the original design at 20∘ oblique incident angle. In addition, the green pepper segmentation results show that wide-angular tolerance optical filter design improves the segmentation of the near-color object about 0.3% at 20∘ oblique incident angle compared to the previous design.

Demonstration of a Human Color Vision Mimic in the Infrared

Color vision results from the interaction of retinal photopigments with reflected or transmitted visible light. The International Commission on Illumination (CIE) developed the CIE color-matching chart, which separates colors on the basis of the interaction of their spectral profiles with three retinal photopigments in the human eye. We report the development of an infrared chromaticity (CIE-IR) chart, which mimics the CIE chart, in order to discriminate between different chemicals on the basis of the interactions of their IR signatures with three different IR optical filters, instead of the retinal photopigments in the human eye. Our results demonstrate that the CIE-IR chart enables separation of different classes of chemicals, as the visible CIE chart does with color, except for those in the IR spectral region. Such results clearly show that the biomimetic sensing method based on human color vision is in fact a true analogue to color vision and that the proposed CIE-IR chart can be used as a classification method unique to this biomimetic sensing modality.

Discrimination Between Explosive Materials and Isomers Using a Human Color Vision-Inspired Sensing Method

This paper describes the application of a human color vision approach to infrared (IR) chemical sensing for the discrimination between multiple explosive materials deposited on aluminum substrates. This methodology classifies chemicals using the unique response of the chemical vibrational absorption bands to three broadband overlapping IR optical filters. For this effort, Fourier transform infrared (FT-IR) spectroscopy is first used to computationally examine the ability of the human color vision sensing approach to discriminate between three similar explosive materials, 1,3,5,-Trinitro-1,3,5-triazinane (RDX), 2,2-Bis[(nitrooxy)methyl]propane-1,3,-diyldinitrate (PETN), and 1,3,5,7-Tetranitro-1,3,5,7-tetrazocane (HMX). A description of a laboratory breadboard optical sensor designed for this approach is then provided, along with the discrimination results collected for these samples using this sensor. The results of these studies demonstrate that the human color vision approach is capable of high-confidence discrimination of the examined explosive materials.

Analytical procedure to assess the performance characteristics of a non-spectroscopic infrared optical sensor for discrimination of chemical vapors

An optical-filter-based sensor that was designed to mimic human color vision was recently developed. This sensor uses three mid-infrared optical filters to discriminate between chemicals with similar, strongly overlapping mid-infrared absorption bands. This non-spectroscopic technique requires no spectral scanning. This paper defines the selectivity and specificity of this biomimetic sensor. Receiver operating characteristic curves are presented for each target chemical. These results demonstrate that the sensor is highly selective and can provide discrimination with no false positives for three similar target chemicals-acetone, hexane, and fuel oil-while rejecting potential interferents.

Biomimetic Optical-Filter Detection System for Discrimination of Infrared Chemical Signatures

Optical-filter-based chemical sensors have the potential to dramatically alter the field of hazardous materials sensing. Such devices could be constructed using inexpensive components, in a small and lightweight package, for sensing hazardous chemicals in defense, industrial, and environmental applications. Filter-based sensors can be designed to mimic human color vision. Recent developments in this field have used this approach to discriminate between strongly overlapping chemical signatures in the mid-infrared. Reported work relied on using numerically filtered FTIR spectra to model the infrared biomimetic detection methodology. While these findings are encouraging, further advancement of this technique requires the collection and evaluation of directly filtered data, using an optical system without extensive numerical spectral analysis. The present work describes the design and testing of an infrared optical breadboard system that uses the biomimetic mammalian color-detection approach to chemical sensing. The set of chemicals tested includes one target chemical, fuel oil, along with two strongly overlapping interferents, acetone and hexane. The collected experimental results are compared with numerically filtered FTIR spectral data. The results show good agreement between the numerically filtered data model and the data collected using the optical breadboard system. It is shown that the optical breadboard system is operating as expected based on modeling and can be used for sensing and discriminating between chemicals with strongly overlapping absorption bands in the mid-infrared.