Artificial intelligence and machine learning algorithms for object detection in the infrared require an extensive amount of high-resolution object-tagged thermal infrared images. Often, acquiring real imagery of sufficient size and range of environmental conditions is difficult due to the cost and time. To address this need, the current study has developed a novel computational framework, i.e. the Sensor Engine, that generates target-tagged synthetic infrared imagery of large complex natural environments. This computational framework, coupled with high-fidelity soil and vegetation thermal physics and geometry models, generates synthetic, high-definition infrared images tailored for High-Performance Computing (HPC) systems. A unique plugin mechanism used to load and unload configured infrared sensors at run time in addition to allowing the framework to effectively work with different sensors in parallel is also discussed. The sensor model within the Sensor Engine communicates with another computational framework to acquire radiative energy for each sensor pixel detector as well as material, distance, source location, and incident angle. To demonstrate the modus operandi of this computational framework, an evaluation and discussion of runtime message passing and test cases are provided.
For realistic synthetic imagery, radiative transfer methods coupled with large mesh geometry provide the most scientifically accurate way to model a scene. Radiative models typically use ray-tracing techniques to determine where radiative energy is coming from or moving to. This work presents an approach to making a ray query Geometry Engine that actively stores large-scale, terabyte-sized geometry in out-of-core memory on parallel general purpose processors. Procedures for geometry distribution, structures for efficient ray-tracing, and the ray query API are discussed. Geometry distribution uses Morton codes with parallel sorting routines to create geometry scene-chunks that are distributed among processing nodes. Each scene chunk is then broken down using a bounding volume hierarchy (BVH) using axis-aligned bounding boxes (AABB). The BVH allows for efficient ray tracing of the geometry. The ray query engine API allows client-side programs, such as sensor models and radiative transfer models, which exist on the same high performance computer to efficiently identify intersected geometry given directed rays and collect individual geometry elements. The geometry has key values that can uniquely identify data from solver programs. Scalability, partition timing, and ray timing results are presented.
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