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Tag: Detection
  • Buried-Object-Detection Improvements Incorporating Environmental Phenomenology into Signature Physics

    Abstract: The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environmental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, this study developed an approach using a Canny edge methodology to identify regions of interest potentially harboring a target object. Finally, an ML method was developed to improve automatic target detection and recognition performance by accounting for environmental phenomenological conditions, improving performance by 50% over standard automatic target detection and recognition software.
  • Spatial and Temporal Variance of Soil and Meteorological Properties Affecting Sensor Performance—Phase 2

    ABSTRACT: 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 interest. 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.
  • Meteorological Property and Temporal Variable Effect on Spatial Semivariance of Infrared Thermography of Soil Surfaces for Detection of Foreign Objects

    Abstract: The environmental phenomenological properties responsible for the thermal variability evident in the use of thermal infrared (IR) sensor systems is not well understood. The research objective of this work is to understand the environmental and climatological properties contributing to the temporal and spatial thermal variance of soils. We recorded thermal images of surface temperature of soil as well as several meteorological properties such as weather condition and solar irradiance of loamy soil located at the Cold Regions Research and Engineering Lab (CRREL) facility. We assessed sensor performance by analyzing how recorded meteorological properties affected the spatial structure by observing statistical differences in spatial autocorrelation and dependence parameter estimates.
  • First Generation Automated Assessment of Airfield Damage from LiDAR Point Clouds

    Abstract: This research developed an automated software technique for identifying type, size, and location of man-made airfield damage including craters, spalls, and camouflets from a digitized three-dimensional point cloud of the airfield surface. Point clouds were initially generated from Light Detection and Ranging (LiDAR) sensors mounted on elevated lifts to simulate aerial data collection and, later, an actual unmanned aerial system. LiDAR data provided a high-resolution, globally positioned, and dimensionally scaled point cloud exported in a LAS file format that was automatically retrieved and processed using volumetric detection algorithms developed in the MATLAB software environment. Developed MATLAB algorithms used a three-stage filling technique to identify the boundaries of craters first, then spalls, then camouflets, and scaled their sizes based on the greatest pointwise extents. All pavement damages and their locations were saved as shapefiles and uploaded into the GeoExPT processing environment for visualization and quality control. This technique requires no user input between data collection and GeoExPT visualization, allowing for a completely automated software analysis with all filters and data processing hidden from the user.