Multi-Temporal IR Thermography For Mine Detection

Yi, Yong; Wen, Maoxing; Wang, Yueming

doi:10.1109/multi-temp.2019.8866906

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…Other techniques in buried object detection include the use of microwave generators (Deans 2001) and IR heaters (Yao et al 2019), which are utilized simultaneously with a passive thermal sensor to compliment and stimulate thermal variability. Yao's team used an IR lamp adjusted to 800 W/m 2 to mimic the natural diurnal cycle of Earth in a controlled indoor environment.…”

Section: Background Environmental Phenomenological Impacts On Thermal...mentioning

confidence: 99%

Modernizing environmental signature physics for target detection—Phase 3

Clausen¹,

Felt²,

Musty³

et al. 2022

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The present effort (Phase 3) builds on our previously published prior efforts (Phases 1 and 2), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried object detection. Environmental phenomenological effects are often represented in weather forecasts in a relatively coarse, hourly resolution, which introduces concerns such as exclusion or misrepresentation of ephemera or lags in timing when using this data as an input for the Army’s Tactical Assault Kit software system. Additionally, the direct application of observed temperature data with weather model data may not be the best approach because metadata associated with the observations are not included. As a result, there is a need to explore mathematical methods such as Bayesian statistics to incorporate observations into models. To better address this concern, the initial analysis in Phase 2 data is expanded in this report to include (1) multivariate analyses for detecting objects in soil, (2) a moving box analysis of object visibility with alternative methods for converting FLIR radiance values to thermal temperature values, (3) a calibrated thermal model of soil temperature using thermal IR imagery, and (4) a simple classifier method for automating buried object detection.

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Section: Background Environmental Phenomenological Impacts On Thermal...mentioning

confidence: 99%

Modernizing environmental signature physics for target detection—Phase 3

Clausen¹,

Felt²,

Musty³

et al. 2022

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show abstract

“…Similarly, Abbott et al (2020) created an effective detection algorithm by using RGB (red, green, blue) imagery for translating noncorresponding LWIR/RGB. Active systems such as ground-penetrating radar (Hendrickx 2006), microwave generators (Deans 2001), and IR heating (Yao et al 2019) utilized simultaneously with the passive thermal sensor complement and stimulate thermal variability. Yao's team used an IR lamp adjusted to 800 W/m 2 to mimic the natural diurnal cycle of Earth in a controlled indoor environment.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal variance of soil and meteorological properties affecting sensor performance—Phase 2

Clausen¹,

Frankenstein²,

Dorvee³

et al. 2021

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An approach to increasing sensor performance and detection reliability for buried objects is to better understand which physical processes are dominant under certain environmental conditions. The present effort (Phase 2) builds on our previously published prior effort (Phase 1), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried-object detection. The study utilized a 3.05 × 3.05 m test plot in Hanover, New Hampshire. Unlike Phase 1, the current effort involved removing the soil from the test plot area, homogenizing the material, then reapplying it into eight discrete layers along with buried sensors and objects representing targets of inter-est. Each layer was compacted to a uniform density consistent with the background undisturbed density. Homogenization greatly reduced the microscale soil temperature variability, simplifying data analysis. The Phase 2 study spanned May–November 2018. Simultaneous measurements of soil temperature and moisture (as well as air temperature and humidity, cloud cover, and incoming solar radiation) were obtained daily and recorded at 15-minute intervals and coupled with thermal infrared and electro-optical image collection at 5-minute intervals.

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“…For the acquisition of each thermographic image, the camera was positioned perpendicular to each of the nine survey zones, as recommended by [4 , 5] . In addition, the images in the database contain information on the specifications of the camera, drone, gimbal and GPS parameters, as well as the configuration of their parameters during acquisitions, which may be of interest in the application of certain techniques.…”

Section: Methodsmentioning

confidence: 99%