Evaluation of generated semi-synthetic signature datasets for training a multiclass object detector (Conference Presentation)

This feasibility study explores training a DNN-based military vehicle detector for airborne guidance systems, addressing challenges of scarce data, numerous similar vehicle classes, and varied real warfare conditions. To this end, a sanitized military vehicle image database is created from multiple 2D and 3D sources with miniature vehicles acquired under various view angles. Complemented by data augmentation tools including AI generated backgrounds, we are able to export controlled, trustworthy and class-equilibrated semi-synthetic datasets. As successful training on a limited number of classes has already been demonstrated, this study further explores the relevancy of this approach on real warfare footage testing with multi-class detector training. By leveraging the combination of data sources and data augmentation techniques and generative AI for creating contextual backgrounds, precision, selectivity, and adaptability of the detectors are evaluated and improved across diverse operational and current situational contexts.