Using Synthetic Data to Accelerate AI Perception Model Development for Object Detection

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Introduction

The concept of operations (CONOPS) for aerial platforms and autonomous systems is evolving rapidly. Radio frequency (RF) jamming can now disrupt communications and global navigation satellite system (GNSS) navigation, increasing the need for multi-class target detection and tracking, onboard autonomy, edge based automatic target recognition and terminal guidance that can operate in contested environments. For OEMs and system integrators, this shift is driving demand for digital perception products that detect, classify, and track mission-relevant objects at the edge using electro-optical (EO) and infrared (IR) cameras.

Generalized object detectors may be sufficient for many situational awareness applications, but many defense applications require fine-grained classification of targets, behaviors, and aimpoints. AI perception solutions adopt a similar ontology to human perception following three levels of perception; detection, recognition and identification. Each level has a pixel on target threshold, and it is often very difficult to collect field data covering all the pixels on target requirements to train a high-performance model. The limiting factor is data as image data is often siloed across military organizations, government agencies, research institutions, and defense contractors, each with different access and commercialization policies. At the same time, target classes continue to expand, and real-world conditions such as camouflage, spoofing, sensor modality, viewing geometry, and environment must be represented in training data. Meeting these requirements demands data generation and model training in days rather than weeks or months. Teledyne FLIR OEM developed AIMMGen™ to address this challenge as a synthetic data generation and AI model training capability that supports the broader Prism™ digital products portfolio.

Key Takeaways

• AIMMGen accelerates object model development by generating large volumes of labeled synthetic EO, IR, and multispectral data for target-rich scenarios.
• The toolchain reduces dependence on scarce, costly, or restricted real-world data sets while preserving the ability to augment measured data when available.
• Automated metadata management, dataset curation, and model optimization support repeatable, traceable development of mission-specific perception models.
• As part of the Prism digital products ecosystem, AIMMGen helps convert synthetic data and training workflows into deployable digital perception capabilities for OEM platforms.
• Example results show measurable performance improvement in air-to-ground detection and demonstrate synthetic-only model training for fine-grained MWIR maritime classification.
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Why Synthetic Data Matters Now

The strategic value of synthetic data for model training is no longer limited to filling training-data gaps. It is becoming a practical way to accelerate software-defined sensing and field adaptable AI perception capabilities. As mission requirements evolve, customers need models that can be expanded to new target classes, validated across sensor modalities, and deployed on embedded processors without restarting months-long data collection and labeling campaigns.

For Teledyne FLIR OEM, AIMMGen is the model-generation foundation behind this digital-products strategy. It helps turn sparse, costly, or difficult-to-collect imagery into validated AI models that support Prism applications for detection, tracking, target recognition, and mission-specific perception.

Synthetic Data and AI Model Generation

Developers in markets such as autonomous driving often have access to large training libraries. In contrast, organizations developing military and mission-specific sensing systems frequently have limited access to representative target data. Real data remains important for precision classifiers, but synthetic data offers a practical way to augment measured datasets or develop fine-grained classifiers when measured data is unavailable, restricted, or prohibitively expensive to collect.

Teledyne FLIR OEM’s AIMMGen (AI Modeling and Model Generation) combines large-scale automated data generation, intelligent data retrieval, and AI model optimization in a single pipeline for Automatic Target Recognition (ATR) algorithm developers and OEM integrators. Within the Prism digital products ecosystem, AIMMGen functions as a model factory: it helps create, train, test, and deliver AI perception models for mission-ready software applications and embedded perception modules. The toolchain rapidly generates synthetic data for real-world scenarios, including target object types, positions, orientations, sensor modalities, configurations, trajectories, and environmental states. It can produce millions of labeled images in days, rather than the weeks or months typically required for field data collection and manual labeling.

AIMMGen automatically generates labels for each image and stores collection metadata with the image set. To support training, validation, and repeatable deployment across use cases, metadata for each image and target is stored in a data lake for rapid curation and dataset construction. This metadata includes collection parameters, target information, and image parameters. AIMMGen’s extensible, model-agnostic training framework then enables cloud-scale training and optimization across a range of use cases and tasks. This traceable workflow helps customers understand not only how a model performs, but also which data, conditions, and assumptions shaped its development.

From Synthetic Data to Deployable Digital Perception

The AIMMGen workflow is most valuable when viewed as part of an end-to-end digital perception pipeline. Synthetic data generation creates labeled imagery at scale. Dataset curation and metadata management make the data searchable and reusable. Automated training and optimization convert curated data into models. Validation and performance testing provide evidence that the model can support a fielded application. In the Prism ecosystem, these outputs can support software products and modules that perform detection, tracking, classification, computational imaging, and target recognition at the edge.

For OEM customers, the challenge is rarely model training alone. The larger goal is to reduce development risk, shorten time to capability, and adapt perception software as targets, sensors, deployment platforms, and mission requirements change.

Step 1:  AIMMGen Synthetic Data Generation Process

The first stage of the AIMMGen process is data generation, which begins with specifications for location types, sensor resolution, target object types, imaging conditions, sensor configurations, and the desired number of images. Randomized combinations of these specifications are created until the total number of images meets or exceeds the desired number. The data generation pipeline engages a geo-specific simulation engine that includes accurate terrain for the globe, sets the environment conditions, inserts targets sampled from over 20,000 unique models, and begins collecting data. Ground truth is automatically generated as metadata and stored alongside target metadata.

Figure 1.  Synthetic Training Data is Generated in Minutes and at Low Cost

Image metadata, including the final URI of each image location and associated target metadata, are captured and stored in an Elasticsearch database. These metadata are stored in separate indexes and linked through unique identifiers, allowing users to visualize, filter, and search across data subsets. After generation, the data can be curated through visualization tools to support experiment design and dataset development.

Step 2:  AIMMGen Model Training and Optimization

The second stage is automated model training and optimization. Users specify datasets, desired model performance characteristics, and optimization metrics. AIMMGen orchestrates a cluster of AI training nodes to train, evaluate, and produce models optimized against baseline and user-defined metrics. Because the training framework is model-agnostic, users can create custom data pipelines and model architectures within AIMMGen. Output models are tracked and stored in a database to support traceability, reproducibility, and future refinement as customer missions or target sets evolve.

Figure 2. Automatic Training and Optimization

Baseline Performance Metrics for AI Perception Model Optimization

In AI perception model development, precision, recall, and F1-score are key metrics for evaluating how well a model identifies and classifies objects or instances.

Precision measures the accuracy of the positive predictions made by the model. It is important when the cost of false positives is high. In military applications, misidentifying a non-threat as a threat could lead to unnecessary actions.

Precision = True Positives/(True Positives + False Positives)

Recall, also known as sensitivity or true positive rate, measures the model's ability to identify all relevant instances. This is critical when missing a positive instance has severe consequences, such as the danger of failing to detect an actual threat.

Recall = True Positives/(True Positives + False Negatives)

F1-Score provides a balanced view of the model's performance, ensuring that both precision and recall are considered. This is a good measure of the model's overall performance when dealing with imbalanced datasets and is essential for developing robust perception systems. 

F1 - Score = 2 x (Precision * Recall)/(Precision + Recall)

AIMMGen Example Applications and Results

AIMMGen has been validated across multiple application and sensor domains, including air-to-ground detection and fine-grained maritime classification.

In an air-to-ground detection experiment, Teledyne FLIR OEM added synthetic electro-optical (EO) and longwave infrared (LWIR) data to an object detection training dataset that already included measured EO and IR data. With only limited changes to the training scheme, the AIMMGen-trained models achieved a measurable improvement in recall. This matters operationally because missed detections can be more consequential than additional false positives in many surveillance, targeting, and force-protection workflows. The improvement is reflected in the overall increase in F1-score.

Table 1.  AIMMGen Models Compared to Real-Only Trained Models

AIMMGen can also support AI model generation with little to no real training data, as demonstrated in a midwave infrared (MWIR) maritime example. Fine-grained maritime object identification typically requires costly MWIR datasets with detailed vessel-type labels. With limited real data, models may only distinguish between broad categories such as “small vessel” and “large vessel.” Using AIMMGen, however, a model was trained exclusively on synthetic data and was capable of classifying specific vessel subtypes. Performance was validated across multiple real-world collections, including fresh water and littoral environments, demonstrating the robustness of synthetic-only training and the potential to expand target classes when real imagery is scarce or difficult to collect.

Figure 3: Fine-Grained Identification in MWIR of Maritime Vessels Using Synthetic Data-Trained AI Models

Summary

Creating large, diverse training datasets for EO, IR, and multispectral applications remains a major barrier to deploying robust AI perception systems. AIMMGen addresses this barrier with synthetic imagery, automated metadata management, dataset curation, and model optimization. As part of Teledyne FLIR OEM’s broader Prism digital products strategy, AIMMGen is more than a data-generation toolchain. It provides a model-generation foundation for developing, validating, and expanding mission-specific AI perception capabilities across defense, autonomy, first responder, and other OEM applications.

For more information about the Prism software family and Teledyne FLIR OEM digital products, please refer to oem.flir.com/prism.