Discover how synthetic data revolutionized our tank detection model training.
Obstacles with Conventional Data in Detecting Tanks
Training a tank detection model using conventional data presents several challenges. One of the biggest obstacles is the scarcity of labeled data. Tanks are not everyday objects, and acquiring enough annotated images for training is extremely difficult due to confidentiality of images.
Additionally, conventional data often lacks diversity. Real-world scenarios can vary greatly, and it’s difficult to capture all possible variations of tanks in different environments, lighting conditions, and angles. This lack of diversity can lead to a model that performs well in controlled conditions but fails in real-world applications.
What is Synthetic Data and Why It Matters
Synthetic data is artificially generated data that mimics real-world data. Unlike conventional data, synthetic data can be produced in large quantities and tailored to specific needs. This allows for the creation of highly diverse datasets that cover a wide range of scenarios.
Synthetic data is crucial for training machine learning models because it provides the volume and variety needed to improve model robustness. Additionally, synthetic data comes fully labelled, so no annotation effort is needed. It also helps in situations where collecting real-world data is impractical or impossible, such as in highly controlled or dangerous environments.
How We Created Synthetic Data for Tank Detection
To create synthetic data for tank detection, we used our procedural engine. Thanks to our proprietary technology, we generated various types of tanks and in different environments. These environments included diverse terrains, lighting conditions, and weather scenarios to ensure a comprehensive dataset. The procedural nature of our engine allows user to control image parameters ranging from the environment, lighting, camera lenses and objects in the image. By setting these restrictions, the engine can generate an unlimited number of images that meet computer vision model’s needs. This huge number of images helps the model learn to focus on the essential features of tanks rather than being influenced by specific visual patterns.
The Impact of Synthetic Data on Model Performance
The use of synthetic data had a significant positive impact on our tank detection model. The model trained on synthetic data demonstrated high accuracy and robustness. It excelled at detecting tanks in various conditions and environments, showing great generalization capabilities. Additionally, the training process became more efficient. With a large and diverse synthetic dataset, the model required fewer training iterations to achieve high performance, saving both time and computational resources.
More Content
Train Computer Vision Models to See Through Fog
In the real world, vision doesn’t stop when the weather turns. But for many computer vision models, fog is enough to break perception entirely. The haze that softens the landscape for human eyes becomes a severe challenge for machine vision—reducing contrast, scattering light, and erasing the fine edges that models rely on to make sense […]
How Synthetic Images Power Edge Case Accuracy in Computer Vision
Edge cases in computer vision are rare, atypical, or safety-critical scenarios that AI models fail to detect reliably because they appear too infrequently in real-world datasets — a camouflaged vehicle in fog, a pedestrian emerging at night, or a partially occluded object. Synthetic image generation makes it possible to produce and annotate these rare scenarios […]
Computer Vision Applications in Military
From boosting surveillance to powering autonomous drones, computer vision is creating a new frontier in defense. Add synthetic image generation to the mix, and you have an innovative combination. Let’s dive into its most impactful applications and how these technologies are reshaping military capabilities. Surveillance and Reconnaissance Effective surveillance forms the backbone of modern defense, […]