Training embedded DNN-based military vehicle detectors for aerial applications with few images using multisource vehicle signatures, data augmentation, and generative AI

This feasibility study investigates the training of DNN-based object detectors for military vehicle detection in intelligent guidance systems for airborne devices. The research addresses the challenges of scarce training images, infrared signatures, and varying flight phases and target distances. To tackle these issues, a database of sanitized military vehicle patches from multiple sources, data augmentation tools and Generative AI (Stable Diffusion XL) are employed to create synthetic training datasets. The objectives include obtaining a robust and performant system based on trustworthy AI, covering vehicle detection, recognition and identification in both infrared and color images within different contexts. In this study various object detection models are trained and evaluated for recall, precision and inference speed based on flight phase and spectral domain, while considering future embedding into airborne devices. The research is still ongoing, with initial results demonstrating the applicability of our approaches for military vehicle detection in aerial imagery.