
Intelligent sensing experts Teledyne FLIR OEM have released a whitepaper detailing; ‘How to Use Automated Synthetic Data Generation in AI Model Development for Object Detection’.
Powered by the company’s proprietary synthetic data generation toolchain, AIMMGen™ (AI Modeling and Model Generation) produces millions of machine-learning-ready examples of military and civilian objects in various environments, weather, and spectral bands.
Synthetic data generation with AIMMGen is a force multiplier for military AI systems, enabling fine-grained target recognition, reducing dependency on classified or hard-to-obtain real-world datasets, and accelerating the deployment of high-performance autonomous defense capabilities.
The Defense Challenge
Modern battlefield dynamics, particularly the vulnerability of radio communications and GNSS to jamming, are driving a shift toward fully autonomous, AI-powered defense platforms. These systems must operate using passive sensor-based perception, relying heavily on AI object detection models. However, developing such models is hindered by the lack of access to large, diverse, and labeled datasets for military targets, especially across different sensor modalities (EO, IR, MWIR, LWIR) and under varied environmental and operational conditions.
Traditional methods of collecting real-world data are slow, expensive, and inadequate for rapidly evolving threats and fine-grained classification needs, such as distinguishing camouflaged or spoofed military vehicles or vessels. Data is fragmented across organizations and often restricted.
Teledyne FLIR OEM’s AIMMGen™ Toolchain
To address this, Teledyne developed AIMMGen, a defense-oriented synthetic data and AI model generation pipeline tailored to Automatic Target Recognition (ATR) and object classification tasks.
Synthetic Data Generation for Military Scenarios
AIMMGen generates millions of labeled images in a matter of days using a geo-specific simulation engine.
- Inputs include military-relevant parameters: target types, sensor configurations, terrain, weather, and orientations.
- Over 20,000 unique 3D target models can be inserted into scenes.
- Metadata, including target ID, location, and environment, is stored in a searchable data lake for curated training datasets.
Automated Model Training & Optimization
- Models are trained on synthetic datasets using scalable cloud infrastructure.
- Framework is model-agnostic, allowing custom architectures.
When developing AI perception models, it’s essential to use specific metrics to assess their effectiveness in identifying and classifying objects or instances. AIMMGen is optimized for metrics such as:
- Precision (reducing false positives)
- Recall (maximizing threat detection)
- F1-score (balancing both)
Defense Implications
AIMMGen provides defense agencies and contractors with a scalable, rapid-response solution to build, optimize, and deploy AI perception models even in data-scarce conditions. This is particularly vital for maintaining superiority in contested environments, where real-time, autonomous target identification can be mission-critical.
Find out more by reading Teledyne FLIR OEM’s latest whitepaper, which provides an indepth look into: ‘How to Use Automated Synthetic Data Generation in AI Model Development for Object Detection’.