Abstract
The Center for Homeland Defense and Security identified an increase of active threat events, such as mass shootings, annually since 1999. Literature suggests that 90% of shootings were over before law enforcement arrived at the scene and the first responder response was limited to “surround and contain” until Special Weapons and Tactics Teams (SWAT) arrived on the scene. Using Unmanned Aircraft Systems (UAS) to detect which individual was the threat and type of weapon used can provide useful information to increase the speed of the response for first-on-scene rather than waiting for SWAT if the type of weapon was known. A UAS equipped with a full spectrum sensor compared traditional red-green-blue (RGB) images to near-infrared (NIR) images in a simulated active threat scenario. A true positive rate (TPR) metric was used to measure the percentage of correctly-detected weapons consisting of either a knife, pistol, rifle, shotgun, or shovel at slant range distances of 25-, 50-, 75-, and 100-feet respectively. A convenience sample of 102 survey participants, recruited from constituents of the Airborne Public Safety Association (APSA) and DRONERESPONDERS was conducted to observe 48 randomly-presented images to determine which type of weapon was detected. The results suggest that survey participants could correctly detect weapons at a 12% greater rate with the NIR sensor than the RGB sensor; however, the pistol had the largest difference in TPR between NIR and RGB sensors. The pistol had an increased probability of detection by 33% when using the NIR sensor compared to an RGB sensor. Additionally, differences were also observed between slant range distances. The closest distance of 25 feet showed a 42% increase in participants’ ability to correctly determine the weapon type compared to the 100-foot slant range distance. Therefore, using a NIR sensor-equipped UAS at flying a maximum slant range distance of 50 feet may help a first-responder determine the type of weapon before SWAT arrives on the scene.
Scholarly Commons Citation
Cerreta, J.,
Denney, T.,
Burgess, S. S.,
Galante, A.,
Thirtyacre, D.,
Wilson, G. A.,
&
Sherman, P.
(2022).
UAS for Public Safety: Active Threat Recognition.
International Journal of Aviation, Aeronautics, and Aerospace,
9(2).
DOI: https://doi.org/10.15394/ijaaa.2022.1686
