This article provides an overview of what computer vision platforms do and why platforms enable broad commercial use. providing the easiest and most agile way to use computer vision technology.
We will guide you through the basics of computer vision and how to speed up computer vision development by using modern automated infrastructure and visual programming.
In particular, the article will cover
- Computer vision technology and its value
- The state-of-the-art and the future of computer vision
- Moving from an algorithm to an application – to make computers see
- Components of a deep learning system
- The premier computer vision infrastructure Viso Suite
About us: Viso.ai provides the leading end-to-end Computer Vision Platform Viso Suite. Global leaders use our computer vision infrastructure to build, deliver, and scale their applications. Get a demo for your organization.
What Is Computer Vision?
Computer vision is the automation of human sight, imitating human eyes with a camera and the brain with a computer. It is a fundamental technology because human sight is mankind’s most important sense; it underlies every human activity. Hence, the ability to teach computers to see opens up massive market opportunities across every economic sector.
Computer vision tasks are available for a variety of applications:
- Image Classification: Categorizing an image into predefined classes
- Object Detection/Object Recognition: Locating and identifying object(s) within an image or video frame. The objects detected are usually designated with bounding boxes.
- Image Segmentation: Identifying specific regions or areas within an image. Two types include instance and semantic segmentation.
- Object Tracking: Locating and following object(s) over time through a series of images or frames.
- Keypoint Detection: Identifying specific points or landmarks in an image representing distinctive features. Often used in image registration and pose estimation.
- Action Localization: Locating and identifying action(s) within an image or video frame. The objects detected are usually designated with bounding boxes.
- Pattern Recognition: Identifying and analyzing patterns within data to extract meaningful insights.
Why Is Computer Vision Important?
Technologically, computer vision is the most advanced field in the modern artificial intelligence (AI) space. And this is about to translate into an enormous commercial value with its climax over the next 5 to 10 years. The computer vision market is projected to reach 27bn by 2028.
Even today, computer vision enables applications across every industry, including agriculture, retail, insurance, manufacturing, logistics, smart city, healthcare, pharmaceutical, construction, and many more. In the years to come, computer vision applications will be applied to a rapidly growing range of industry-specific use cases to automate products and services.
Future of Computer Vision Technology
The enormous success of computer vision started in 2012 with the introduction of deep learning and powerful GPUs that allow parallelized computing. The next step to making computer vision broadly available (AI democratization) is the megatrend Edge AI, moving machine learning tasks (ML) from the cloud to the source of data.
Deploying computer vision ML to edge devices makes it possible to overcome the limitations of pure cloud solutions: privacy, costs, accessibility, latency, data transfer volume, and robustness.
On-device inference allows robust real-time applications. A prominent example of Edge AI vision is autonomous driving which requires offline-robustness and ultra-low latency. We enter a huge deployment phase for deep learning. The AI chip market is booming and expected to grow from 5bn to 22bn in 2025.
How Computer Vision Works
In a nutshell, computer vision allows computers to “see” and process visual media with image recognition algorithms. Deep learning algorithms can easily be trained with annotated data where humans draw shapes for specific classes (“car,” “human,” “dog”) in every image, and neural networks are trained on it. The trained image recognition algorithm can then find and return those classes.
The most popular image recognition algorithms (e.g., YOLOv3, YOLOR, VGG, ResNet) are pre-trained and benchmarked on massive public datasets with already annotated images (such as Microsoft COCO or Google OID). Image annotation is the technique to label digital images (image tagging) or video frames manually to provide ground-truth data that can be used to train a machine-learning algorithm.
Components of a Computer Vision System
A modern deep learning computer vision system typically contains the following components:
- Image acquisition: The video stream of a camera or a video file needs to be grabbed frame by frame (every image is processed individually).
- Pre-processing: The image is optimized or cropped to improve algorithm performance.
- Algorithm: Deep learning algorithms perform various computer vision tasks.
- Decision-making logic: Conditional logic to handle the algorithm’s output (pass/fail), count, and aggregate the classes.
- Communication: Sending the information to the cloud to store in a database and visualize it in dashboards.
How Can Companies Use Computer Vision? – Make or Buy
Due to the disruptive nature of computer vision, organizations across industries strive to adopt the technology and employ it to solve various problems (AI vision inspection, remote monitoring, counting, quality control, event recognition, etc.). Hence, innovation teams face a make-or-buy decision with the following options
- 1) use a ready-made yet inflexible turnkey product,
- 2) develop everything from scratch using open-source tools, or
- 3) use a computer vision platform.
Develop Computer Vision Systems From Scratch
From Fortune 500 enterprises to AI startups, many organizations build their AI vision systems with a computer vision platform to avoid coding everything from scratch, integrating incompatible software platforms, and writing hard-to-maintain code.
Most companies start developing with traditional methods before moving projects from PoC to production becomes a huge challenge.
Many Companies Experiment With Open Source
Today many great computer vision tools and software are free (for example, OpenCV or OpenVINO). Over 90% of current computer vision applications, many AI services, and commercial products are based on open-source tools.
Such open-source software for image annotation (CVAT, LabelImg), and machine learning frameworks (TensorFlow, PyTorchVideo, etc.) make it simple to train and run a deep learning model. In fact, running an AI model can be done in as little as 72 lines of code.
However, developing an AI vision that can be effectively and safely used, scaled, and maintained, is highly complex and very challenging. The challenge is to integrate them in a sustainable and agile way, especially since technology ages faster than ever.
Challenges of Do It Yourself
When disruptive technologies emerge, companies are often tempted to try developing everything from scratch. For example, when the first digital customer relationship management (CRM) products were introduced, many large scale enterprises attempted to create their own CRM software. However, due to the complexity and maintenance costs, many failed, re-evaluated the options, and eventually ended up purchasing a popular CRM platform such as Salesforce, Microsoft Dynamics, or Pipedrive.
The same trend can be observed with computer vision, where many companies across industries hire teams of computer vision engineers with the goal of developing and maintaining their own AI vision systems internally. Yet, compared to a CRM or a web/cloud application, computer vision is significantly more complex and requires knowledge of advanced computing across different disciplines.
Computer vision requires knowledge of hardware, optical sensors, Edge Computing, the Internet of Things, Cloud Computing, Web Development, MLOps, Machine Learning, and Image Processing. It’s hard to bring those different fields of software together.
Expensive Complexity and Technical Debt
Often, companies end up failing to integrate the different tools, incompatible platforms, hardware/software, and data models. If different software tools are patched together, the complexity increases with the number of integrations that are hard and expensive to maintain (“spaghetti code”). The solutions usually work as proof of concept, but because changes and refactoring are needed over time, it becomes much more difficult and costly to maintain the software (technical debt).
Talent and expertise are scarce, and most engineers lack sufficient experience in running computer vision in production. Consequently, software engineering and especially machine learning experts are very expensive. And delays or unexpected issues further drive the costs.
The rapid technological advances drive the need to stay agile and be able to update software and hardware to realize enormous efficiency gains (Cost/Frames per second, Watt/Frames per second). If AI vision is used for mission-critical applications, such as visual inspection or remote monitoring of business processes, production-grade systems require robust updating, agile development, release management, security, hardware management, identity management, and more.
If you are interested in learning more about the cost drivers of computer vision, and how to save costs, check out our guide “What Does Computer Vision Cost?”.
Develop Using a Computer Vision Platform
Using a computer vision platform, companies can significantly:
- Accelerate time to result
- Lower operating costs
- Increase agility
- Improve the odds of successfully implementing computer vision.
With the growing importance of computer vision, most companies will end up needing multiple AI vision applications that are highly specialized for different use cases.
Therefore, computer vision platforms provide a way to provide the ability to rapidly implement different and highly customized AI systems without the need to write code from scratch and maintain integrations. Using a computer vision platform allows teams to build internal skills and receive access to very powerful AI technology to solve previously impossible business problems.
Hence, AI vision platforms provide a way to adopt AI vision technology while achieving vastly greater cross-learning, synergies, agility, and cost-efficiency. Platforms provide the ability to tailor systems towards specific use cases and environments, to achieve maximum performance and cost-efficiency. Modular and low-code/no-code development platforms make it possible to integrate and replace technology components (ML models, logic, etc.) without rewriting the entire application.
Viso Suite Computer Vision Infrastructure
In the following, we will describe our hands-on experience and why we have built a computer vision platform to democratize AI. The Viso Suite software infrastructure integrates everything needed to create a custom application with logic flows, deploy it to physical edge devices with full device management, and monitor metrics sent to the cloud in custom dashboards.
Automated AI development does not require manual code writing. An intuitive visual interface with pre-built modules is offered instead – resulting in significant time savings.
Computer Vision Platforms Manage Complexity
At viso.ai, we’ve built an automated end-to-end platform to build and orchestrate computer vision applications. The user can prototype and scale applications from staging to production, with remote debugging and lifecycle management tools (device management, release management, access, and security control). Before, companies ended up with a repository of code that was hard to reuse and maintain.
Therefore, Viso Suite includes features promoting accelerated development with intuitive visual programming, making it possible to build the application pipeline with pre-built modules that can be quickly exchanged. An integrated marketplace makes it possible to import pre-built application templates, for example, for people counting or animal tracking. The entire platform provides an end-to-end infrastructure to run computer vision projects much faster and at lower costs, with the ability to port across architectures and exchange the algorithm quickly.
All applications are deployable to edge devices, and metrics of the applications are gathered in the cloud in custom dashboards.
Why Use a Computer Vision Platform
Using computer vision and running an AI model is simple until you have to worry about developing the logic around your AI model. It’s a hassle getting the video streams from different sources, deploying and storing the model and data, running optimized DL models in production, using the collected data, and making it useful in dashboards – to create business value from an algorithm.
- It grows in complexity when deploying apps from the cloud to edge devices and managing a distributed fleet of locations. This is required in most real world because of privacy, performance, and robustness.
- Managing a computer vision application in production requires battle-tested release management, version management, security, access management, monitoring and debugging, and edge-to-cloud data connectors with offline buffering.
- CV applications usually involve complex workflows around the DL model determining the application performance greatly. These include image-cropping, frame-buffering/-skipping, parallelized processing, etc.
Extensive, custom development leads to massive, complex code repositories that are difficult to continuously maintain. And they are often not reusable.
Enter: Viso Suite
- As a highly intuitive infrastructure, Viso provides a visual editor with drag-and-drop functionality. This allows teams to build and update applications much faster with pre-built modules. Over 2’500 function nodes are included to send emails, SMS, slack messages, and more.
- Viso Suite provides all the tools and infrastructure needed to manage the process of scaling computer vision applications. This process includes all steps from staging to production, with all debugging and lifecycle management tools. I.e. device management, release management, access management, etc.
- Viso Suite supports a long list of all state-of-the-art frameworks, such as TensorFlow, PyTorch, and Torch. It includes an extensive list of image classification and object detection algorithms, including YOLOv3, YOLOv7, YOLOR, SSD, PoseNet, ResNet, etc.
- Viso Suite infrastructure powers real-world computer vision applications requiring optimized AI models for on-device machine learning. These include TensorFlow Lite or Lightweight OpenPose.
- Applications built on Viso are portable. With a single click, users can convert video files to an IP camera, a USB camera, or a multi-camera. Then, you can select the processing chip (VPU, TPU, GPU, CPU) for combined use (parallelized processing).
Advantages: Much faster, dramatically simpler, and agile – therefore reduced costs and robust architecture.
Case Study – Object Detection Application
A leading IT technology provider used Viso Suite to deliver a computer vision project for a large European airport. After one week of using and learning Viso Suite, they built a complex vision application. They used pre-installed CCTV cameras to detect a large number of transport trolleys and visualize their status, loaded vs. unloaded. This was managed completely in a custom real-time dashboard, all on Viso Suite.
The user reported significant time savings. Visual tools simplify the creation and updating of workflows with pre-built modules. The ability to skip writing manual code saves a lot of time. Releasing updates and computer vision deployments are easy and do not require CLI/terminal.
What’s Next?
Computer vision infrastructure provides a powerful way for organizations to deliver end-to-end computer vision projects. Automated features help to speed up computer vision development drastically. Managed and integrated infrastructure helps businesses deliver computer vision in retail, manufacturing, logistics, healthcare, transportation, and other industries.
Viso.ai is a partner of Intel, NVIDIA, and HP Enterprise. Industry leaders use our software platform to integrate and scale computer vision efficiently.
Contact us to schedule a personal demo.
