MLops and Synthetic data for national security

Dropla's MLOps team provides the systems, tools, synthetic training datasets and processes to reliably deploy machine learning for your operations across the industries.

We facilitates the smooth lifecycle management of ML pipelines, from development to deployment and monitoring.

first CASE: LANDMINE DETECTION

Synthetic data sets

Our simulation tooling and photorealistic 3D rendering pipelines allow us to create vast labeled datasets featuring diverse environments, terrain, weather conditions, and ground objects: landmines, UXOs and ERWs.

Real world data sets

Dropla is collecting and creating large amounts of labaled datasets, by utilizing Dropla HUB-194 testing site. And it is our duty to contribute standardized data to global datasets. This would help the wider research community and spur more data-driven innovations in Global De-mining initiatives.

ML-op pipeline for ground-truth dataset
synthetic augmentation and training

5000 Ground truth dataset

1. First step

Ground truth normalization

2.Dataset synthetic augmentation

3. Creating data with adjustments

Automated labeling

4. Learning

Dropla AI Helper

5. Testing

Dropla Mine Detection Tool

40 000 Synesthetic augmented data, labored frames

Process

1. Creating Model

Developing highly detailed and accurate 3D models of defense equipment to be used in simulations and training scenarios.

2. Texturing

Applying realistic textures to 3D models to enhance visual fidelity and ensure accurate representation of defense assets.

3. Creating Environment scenes

Constructing realistic and diverse environmental scenes to simulate various operational terrains and scenarios for training and analysis.

4. Rendering more than 2000+
variety photos

Generating a vast array of high-quality images from different angles and settings to create a comprehensive dataset for training AI models.

5. Automated labeling

Automated accurately annotating the rendered images with relevant metadata to facilitate effective machine learning and data analysis processes.

6. AI pipeline injection

We construct custom architectures for seamless injection of the labeled dataset and training of AI models - to recognize and interpret equipment, environments, and scenarios accurately.

7. Testing

Rigorously testing the trained AI models to evaluate their performance, accuracy, and reliability in identifying and analyzing defense-related data.

8. Implementing and adjustment

Deploying the AI models into operational systems and making necessary adjustments to optimize performance and ensure they meet defense standards and requirements.

Dropla MLops tooling

Dropla ML Ops team is developing tooling, easing every stage of AI solutions deployment: creating/preparing data and building AI pipelines.

With us your organization can achieve quick early results in AI deployment in high value cases like:

- Localization

- Multi-object tracking

- Vehicle awareness with dynamic mapping

Our Services

Labeling Different Types of Ammunition and Mines

We provide high-quality labeling and classification services for various types of ammunition and mines.

Creating Synthetic Objects for AI Training

We develop realistic synthetic objects used to train neural networks. These models help AI better understand and recognize potential threats in real-world conditions.

Discover how our
dedication can
help your cause

Get in touch Join our team