Massively Parallel Synthetic Sensor-Based Infrared Image Generation for Object Detection

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.