Proceedings Article | 24 April 2020
KEYWORDS: RGB color model, Long wavelength infrared, Sensors, Visible radiation, Cameras, Machine learning, Infrared radiation, Infrared imaging, Infrared detectors, Imaging systems, Unmanned aerial vehicles, Automatic target recognition, Target detection
There is an increasing demand for technology and solutions to counter commercial, off-the-shelf small unmanned aerial systems (sUAS). Advances in machine learning and deep neural networks for object detection, coupled with lower cost and power requirements of cameras, led to promising vision-based solutions for sUAS detection. However, solely relying on the visible spectrum has previously led to reliability issues in low contrast scenarios such as sUAS flying below the treeline and against bright sources of light. Alternatively, due to the relatively high heat signatures emitted from sUAS during ight, a long-wave infrared (LWIR) sensor is able to produce images that clearly contrast the sUAS from its background. However, compared to widely available visible spectrum sensors, LWIR sensors have lower resolution and may produce more false positives when exposed to birds or other heat sources. This research work proposes combining the advantages of the LWIR and visible spectrum sensors using machine learning for vision-based detection of sUAS. Utilizing the heightened background contrast from the LWIR sensor combined and synchronized with the relatively increased resolution of the visible spectrum sensor, a deep learning model was trained to detect the sUAS through previously difficult environments. More specifically, the approach demonstrated effective detection of multiple sUAS flying above and below the treeline, in the presence of heat sources, and glare from the sun. Our approach achieved a detection rate of 71.2 ± 8.3%, improving by 69% when compared to LWIR and by 30.4% when visible spectrum alone, and achieved false alarm rate of 2.7 ± 2.6%, decreasing by 74.1% and by 47.1% when compared to LWIR and visible spectrum alone, respectively, on average, for single and multiple drone scenarios, controlled for the same confidence metric of the machine learning object detector of at least 50%. With a network of these small and affordable sensors, one can accurately estimate the 3D position of the sUAS, which could then be used for elimination or further localization from more narrow sensors, like a fire-control radar (FCR). Videos of the solution's performance can be seen at https://sites.google.com/view/tamudrone-spie2020/.
