Multiverse Computing partnered with a consortium of aerospace and technology companies to bring real-time object detection capabilities to stratospheric platforms operating at the limit of power, memory and connectivity budgets. By compressing a state-of-the-art detection model to fit inside dedicated FPGA memory, the project turns high-altitude platforms into autonomous observation assets and removes the dependency on cloud or ground processing for the inference step.
The Challenge
Deploying real-time object detection from stratospheric platforms required extremely low power and memory consumption while maintaining reliable accuracy. The target hardware, FPGA-based and fully on-device, imposed tight constraints that standard detection models could not meet. Without compression, the model architecture was too large and too resource-intensive to fit into dedicated FPGA memory, limiting real-time performance and forcing the platform to rely on cloud or ground compute for any vision workload.