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
6 Steps to Train Your Computer Vision Model with Synthetic Images
In computer vision, developing robust and accurate models depends on the quality and volume of training data. Synthetic images, generated by procedural engine, have emerged as a transformative solution to the data bottleneck. They empower developers to overcome data scarcity, reduce biases, and enhance model performance in real-world scenarios. Here’s a detailed guide to training […]
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 […]
A Practical Guide to Labels Behind Computer Vision Models
In defense and security applications, where precision, reliability, and situational awareness are critical, the performance of computer vision models depends in 80% on the inputted labeled data. Annotation is the process of adding structured information to raw image or video data so that AI systems can learn to interpret the visual world. It enables models […]