Blog

AI Verse and Soloma Avionics are Finalists for DAIC Partnership of the Year Award

Recognizing joint innovation in thermal UAV detection for frontline defense

When AI Verse and Soloma Avionics began working together, our shared goal was clear: improve thermal detection performance where it matters most – saving lives in Ukraine.

Our partnership has now been recognized by the Defence AI Council as the Finalists for Partnership of the Year award. It honors projects that show measurable operational impact through effective collaboration between AI developers and defense technology companies.

Why Detection Matters

Shahed drones are a persistent and lethal threat. With thousands launched every month, detecting them early, especially under low‑contrast night‑time conditions, is essential.

Soloma’s existing detection system had solid benchmark scores but struggled against the unpredictable conditions of the field. To strengthen it, AI Verse used our procedural synthetic data engine to generate an infrared dataset that mirrored real mission environments.

The Synthetic Edge

By fine‑tuning Soloma’s models with our synthetic infrared Shahed‑136 dataset, the team achieved:

  • Detection recall boosts from 9.4% to 51%, later stabilizing at 42% in field conditions
  • Zero false positives
  • 100% detection precision
  • 1.13 s faster first detections, giving operators up to 23 m more response distance

These improvements mean earlier awareness, faster reaction, and reduced operator fatigue under pressure.

Shared Impact

This project shows what’s possible when synthetic data and field experience meet with purpose. We are thankful to Soloma’s engineers for their deep commitment and to DAIC for recognizing the effort.

More Content

images for resource pages miniatures 4 3 – How We Leveraged Synthetic Images to Train a Fall Detection Model | AI Verse
Blog

How We Leveraged Synthetic Images to Train a Fall Detection Model

In the development of a computer vision fall detection model, one of the biggest challenges is obtaining high-quality, well-annotated image datasets. Real-world fall datasets are scarce due to privacy concerns, ethical constraints, and the difficulty of capturing diverse fall scenarios in real life. We tackled this challenge by leveraging synthetic images to train a highly […]

images for resource pages miniatures 19 – AI Verse and Soloma Avionics are Finalists for DAIC Partnership of the Year Award |
Blog

AI Verse and Soloma Avionics are Finalists for DAIC Partnership of the Year Award

Recognizing joint innovation in thermal UAV detection for frontline defense When AI Verse and Soloma Avionics began working together, our shared goal was clear: improve thermal detection performance where it matters most – saving lives in Ukraine. Our partnership has now been recognized by the Defence AI Council as the Finalists for Partnership of the Year award. […]

images for resource pages miniatures 4 – How to Evaluate a Synthetic Image Dataset Specification for Training a High-Performa
Blog

How to Evaluate a Synthetic Image Dataset Specification for Training a High-Performance Computer Vision Model

In the domain of computer vision, the dataset’s relevance, quality, and diversity are key drivers in achieving high accuracy and reliable performance. A well-specified synthetic dataset doesn’t just enable effective model training; it sets the foundation for the model’s success in challenging, real-world scenarios. This guide outlines seven essential pillars for evaluating synthetic datasets: relevance […]