Have you ever wondered about how synthetic data get used for computer vision in practice?
Or maybe, how can we generate synthetic data to enhance computer vision?
Synthetic data helps us better understand the object or scene getting observed. It’s also a prerequisite for building novel algorithms for computer vision systems, but this is just a general talk.
Let’s find out more about synthetic data in detail.
What is computer vision?
A computer vision system uses visual inputs and digital images to derive meaningful information before taking action. The ability to see, observe, and understand is enabled by computer vision, in a similar way to how artificial intelligence allows computers to think.
There are a lot of similarities between computer vision and human vision, except that humans have the advantage of years of experience training it to tell apart objects. How far away they are? and whether they are moving, or whether an image is correct or wrong.
On the other hand, computer vision systems get designed to train machines to perform these tasks. It has to function much faster than retinas, optic nerves, and visual cortex do because it uses cameras, data, and algorithms instead of retinas, optic nerves, and visual cortex.
It is possible for systems designed to inspect products or watch production assets to detect imperceptible defects or issues in thousands of products per minute. This allows them to surpass human capabilities quickly.
What Is Synthetic Data In Machine Learning And How To Generate It?
Synthetic data relates to data generated through machine learning algorithms that mimic real-world patterns.
The following sources identify synthetic data for a variety of purposes and types:
- Images and videos
Computer vision uses synthetic images in many forms like RGB images, segmentation maps, depth images, stereo pair images, LiDAR images, and infrared images.
A generative ML model gets used to create synthetic images and videos that mimic the potential space of real-world images and videos.
A variety of generative models get used to generate synthetic data, including:
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Autoregressive models
Synthetic data provides a variety of benefits, including reducing constraints. It is necessary to tailor sensitive or regulated data to specific conditions to achieve the results that authentic data cannot deliver. Additionally, providing DevOps teams with datasets to test and confirm software.
Computer vision can generate synthetic data in two ways.
Methods Based On Deep Learning
By (Gans) Generative Adversarial Networks
In GAN, two neural networks (called generator and discriminator) compete against each other in a zero-sum game. There is a win for one agent and a loss for the other.
By Variational Autoencoder
VAE models compress the actual dataset before transmitting it to the decoder.
Afterward, the decoder produces an output representing a real dataset.
During training, input and output data get correlated to optimize the system.
A 3D Rendering-based methods
To generate SDs using 3D rendering, follow these steps:
- Render synthetic images for model training by generating and placing 3D models of objects in a simulation, setting up the environment (camera viewpoint and lighting), and procedurally creating an object in a 3D model.
- Then extracting photorealistic images from a photorealistic virtual world process.
- A 3D renderer will automatically annotate synthetic data after getting rendered in 3D.
How Can Synthetic Data Help Computer Vision?
Deep learning has to get applied to several fields, including automotive, healthcare, and security.
A computer that can interpret visual data can produce significant results. Self-driving cars and accurate disease detection are a few examples of this system.
Synthetic data can get used to assist computer vision in the following ways.
Streamlines And Reduces The Cost Of Creating Datasets.
Data collection for real-world visuals with desirable characteristics and diversity can be time-consuming and extremely expensive. In order to achieve accurate model outcomes, data points must get annotated with the correct labels after collection.
These processes can consume valuable business resources and take months to complete. Synthetic data get generated programmatically, so no manual data collection is necessary, and annotations can be nearly error-free.
Provides The Ability To Predict Rare Events.
Real-world datasets are often imbalanced, resulting in rare events getting overrepresented.
Even so, this does not mean they aren’t necessary. For instance, the self-driving car with a computer vision system that will learn from accidents on the road may not have enough examples since it is a little hard to collect visual data.
Computer vision applications typically encounter rare diseases and counterfeit money.
To enable safer and more reliable self-driving cars, we can train deep learning algorithms on synthetic images and videos of car accidents under various circumstances (different times of day, multiple vehicles, pedestrians, etc.).
Hence, synthetic data allow us to generate datasets based on real-world events that are more accurate representations of these events.
Ensures The Privacy Of Personal Data.
Visual data is also challenging to collect and store because of data privacy regulations like GDPR.
Businesses can lose their reputations and get fined for non-compliance. As a result of working with datasets containing sensitive data, there is a risk of a data breach. It is true even when model results get used to prove a point.
With the help of APIs and the person’s name, researchers extract recognizable face images from the training set. Since synthetic data does not contain information about real people, it eliminates the risk of privacy violations while preserving the essential characteristics of actual data.
In conclusion, we understood that one of the challenges in Computer Vision is creating training/testing datasets, but having a sufficiently large dataset is required to get accurate results; this is where synthetic data comes in.
A synthetic dataset can mimic real-world data distribution and enhance real datasets compared to an augmented dataset. With GANs, VAEs, and 3D rendering models, synthetic data can get generated without much human involvement.
Among the many fields in which synthetic data get used are autonomous driving, healthcare, and retail is few of them.