Chemical agent standoff detection and identification with a hyperspectral imaging infrared sensor

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.

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Design of a Cooperative Chemical Agent (CA) Detection Algorithm with the Hyperspectral Imaging System

10.23919/iccas52745.2021.9650013 ◽

2021 ◽

Author(s):

Hyeong-Geun Yu ◽

Whimin Kim ◽

Dong-Jo Park ◽

Dong Eui Chang ◽

Hyunwoo Nam

Keyword(s):

Hyperspectral Imaging ◽

Imaging System ◽

Detection Algorithm ◽

Chemical Agent

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Hyperspectral imaging for the elimination of visual ambiguity in corrosion detection and identification of corrosion sources

Structural Health Monitoring ◽

10.1177/14759217211041690 ◽

2021 ◽

pp. 147592172110416

Author(s):

Dayakar N Lavadiya ◽

Hizb Ullah Sajid ◽

Ravi K Yellavajjala ◽

Xin Sun

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Sulfate Solution ◽

Vital Role ◽

Mitigation Strategies ◽

Svm Classifier ◽

Corrosive Media ◽

Detection And Identification ◽

Coated Surfaces ◽

Visual Ambiguity

The similarity in the hue of corroded surfaces and coated surfaces, dust, vegetation, etc. leads to visual ambiguity which is challenging to eliminate using existing image classification/segmentation techniques. Furthermore, existing methods lack the ability to identify the source of corrosion, which plays a vital role in framing the corrosion mitigation strategies. The goal of this study to employ hyperspectral imaging (1) to detect corroded surfaces under visually ambiguous scenarios and (2) identify the source of corrosion in such scenarios. To this end, three different corrosive media, namely, (1) 1M hydrochloric acid (HCl), 2) 3.5 wt.% sodium chloride solution (NaCl), and (3) 3 wt.% sodium sulfate solution (Na2SO4), are employed to generate chemically distinctive corroded surfaces. The hyperspectral imaging sensor is employed to obtain the visible and near infrared (VNIR) spectra (397 nm–1004 nm) reflected by the corroded/coated surfaces. The intensity of the reflectance in various spectral bands are considered as the descriptive features in this study, and the training and test datasets were generated consisting of 35,000 and 15,000 data points, respectively. SVM classifier is trained and then its efficacy on the test data is assessed. Furthermore, validation datasets are employed and the generalization ability of the trained SVM classifier is verified. The results from this study revealed that the SVM classifier achieved an overall accuracy of 94% with the misclassifications of 18% and 13% in the case of NaCl and Na2SO4 corrosion, respectively. Reflectance spectra obtained in the VNIR region was found to eliminate the visual ambiguity between the corroded and coated surfaces and, identify the source of corrosion accurately. Further, the range of key wavelengths of the spectra that play an important role in the distinguishability of coating and chemically distinctive corroded surface were identified to be 500–520 nm, 660–680 nm, 760–770 nm, and 830–850 nm.

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