Diagnosis of aerial detectors with generative models

In the field of computer vision applied to remote sensing, the evaluation of vehicle detectors in aerial images has historically relied on real datasets, which are expensive to label and limited in variety of weather, lighting, or scene conditions. A recent advance proposes the use of large-scale generative image models to create controlled synthetic test benches, where it is possible to isolate specific variables and accurately measure detector behavior in scenarios difficult to replicate in the real world. This approach not only allows diagnosing weaknesses in detection architectures —such as YOLO, Faster R-CNN, or Transformer-based— but also guides targeted data augmentation strategies, achieving significant improvements in precision (AP50) with a much smaller number of additional samples. The methodology combines textual scene generation, controlled attribute editing, and automatic verification, all orchestrated within a modular framework that can integrate the latest language and vision models. For companies working on developing solutions of AI for businesses, this approach represents an opportunity to apply custom software that leverages generative artificial intelligence as a tool for validation and continuous improvement. At Q2BSTUDIO, we offer business intelligence services, integration of AI agents, and cybersecurity capabilities for cloud environments, whether with AWS and Azure cloud services, all tailored to the specific needs of each project. The synergy between synthetic tests and real data, along with the use of Power BI to visualize diagnostic results, allows organizations to make informed decisions about data collection and model training. Thus, the combination of custom applications and cutting-edge technology drives the reliability and efficiency of detection systems in the aerospace and defense sector.