Visionary bytes
Despite its critical role in advancing modern industry, the complexities and challenges of data management in machine vision and deep learning remain largely overlooked. However, further data processing offers great opportunities, as Martina Chmelícková, CBO at DataVision, outlines in this article.
In the world of industrial technology, machine vision and deep learning are changing the game. More and more people are asking for help with managing their image data, especially as machine vision gets more popular. This shows that knowing how to handle data well is becoming important in these areas.
Handling image data for machine vision and deep learning is complex. Data accuracy and dataset quality are crucial for training deep learning models or the development of robust machine vision applications. Managing data in these fields means dealing with a lot of information. There is more to it than just keeping things in order – it is also about making sure the data is safe and private, especially when it comes to images. Getting this right is key to solving today’s tech challenges and shaping how we will manage data in the future.
In a lot of companies that use machine vision, there is a big challenge with keeping their data organized. Often, the image data is just stored on different hard drives without much order. Sharing this data between teams in the same company can be tough, too. Most times, they need the right setup to easily send or access data, which makes working together harder than it should be.
Also, moving image data to the cloud isn’t always easy. The setup of companies’ computer systems makes this pretty challenging. But more and more companies are realizing they must find a better way to handle all this data. They are looking for good solutions to simplify using the cloud, so they can do more with their AI and machine vision projects.
Navigating the data maze in machine vision
In the world of machine vision, managing data is like trying to find your way through a maze. It is not just about having the right data; it is about using it well in real situations. When businesses start working with machine vision, they soon find out that managing data is a whole new discipline. The usual ways of handling data don’t really work when you are dealing with images and videos. It is not just about putting these huge amounts of data in order. The bigger challenge is moving it, sharing it, and using it all over the world.
The significance of maintaining well-organized data has gained extraordinary importance with the emergence of deep learning. Deep learning models consistently outperform traditional computer vision algorithms in numerous machine vision tasks. The application of deep learning has even provided solutions to complex tasks that lacked satisfactory resolution before. The utilization of deep learning in machine vision is particularly appealing in areas such as quality control, text reading, object detection, and similar applications.
However, training a high-performing deep learning model comes at a cost. The primary challenge lies in constructing the right dataset – one that is sufficiently large, includes a diverse set of images, maintains a balanced distribution of classes, and incorporates high-quality annotations. To accomplish this, a company must be well-prepared in terms of its data collection pipeline and data management tools.
You see: We’re moving into a time when being good at managing data is important for making the most of machine vision.
Well-defined machine vision pipeline
How can a machine vision workflow benefit from data management? Whether dealing with traditional machine vision methods or deep learning, the journey begins with defining datasets. These datasets serve the purpose of creating a model and verifying its robustness. In simpler terms, it is necessary to identify a set of images that is used during algorithm development (in the case of traditional machine vision) or for training a deep learning model. The creation of high-quality datasets can be greatly enhanced by using the right data management tool.
Such a tool should provide data organization features, including simple data ingestion (with real-time data collection), data augmentation, image annotation, collaboration and sharing, dataset splitting (train-validation-test), version control, API access for integration, and more. A well-defined machine vision pipeline ensures seamless integration of all utilized tools.
For example, if HALCON from MVTec is used for machine vision, it should be straightforward to work with images fetched directly from a data management tool. Developers should not be burdened with technical details such as the source of the data or whether the data is in its latest version. Another feature commonly used among machine vision engineers is the ability to visualize the results of an algorithm. The data management tool should offer the option to “overlay” the image with a visualization of the result produced by an algorithm. All these features significantly enhance the developer experience, allowing developers to focus on what matters most – the creation of the machine vision algorithm itself. There are already specialized data management platforms available, as for example Bee-Yard.ai by DataVision that are designed to address the challenges described above and provide tailored solutions for machine vision applications.
The future of data management in machine vision
Concluding our discussion on machine vision data management, the emerging landscape is marked by significant advancements in AI, machine learning, and improved network infrastructures like 5G. The evolution of machine vision is closely tied to advancements in managing data. This means, data management will shape the future applications of this technology.