AI has the potential to add $13 trillion to global GDP by 2030. Gross value added boosts are expected in all sectors, although manufacturing, professional services, and wholesale and retail have the biggest opportunity to derive value from AI. Computer vision is likely to help drive much of that value.
Computer vision is the ability for a computer to see and understand the physical world. With computer vision, computers can learn to identify, recognize, and pinpoint the position of objects - thereby impacting things in the real world. Today computer vision is already used for gas leak detection, bean counting, basketball shot tracking, identifying sushi, steel manufacturing throughput, and much more.
Today we'll explore how computer vision platforms are transforming the way businesses approach computer vision, and how you can benefit from leveraging these powerful tools.
Computer Vision Platforms Simplify Complexity
As businesses explore the potential of computer vision, managing the complexity of building and deploying these systems can be daunting. Computer vision platforms play a crucial role in simplifying this process by offering tools and services that streamline the development pipeline.
What is computer vision?
Computer vision is helping a computer make sense of images - image recognition, identifying contents of what's in an image - and make sense of video. Ultimately a computer can achieve these tasks with greater speed, consistency, and scalability than humans. Computer vision solutions break down into a number of different techniques: classification and object detection, semantic segmentation, keypoint detection, and new techniques that emerge every day.
Classification: This is a traditional machine learning technique used to categorize data into specific classes. In the case of images, it involves adding labels (or tags) to the image. For example, you might label an image as containing either a dog or a cat, essentially tagging the image with a class.
Object Detection: This technique is similar to classification, but it provides more detailed information. Instead of just labeling an image, object detection identifies and locates specific objects within it. For example, if you have a picture with multiple dogs, object detection would draw a box around each dog, telling you not only that there are dogs, but also where they are located in the image. This added detail enables tasks like counting objects.
Segmentation: This is a more advanced technique that goes beyond object detection by outlining the exact shape of objects in an image. It involves drawing precise masks around the contours of each object, providing a pixel-by-pixel annotation. This level of detail is especially useful when you need accurate measurements of areas or specific parts of the image.
Imagine you have a field of tomato plants and want to analyze them. The approach you take depends on the level of detail required:
- Classification: If your goal is simply to determine whether a tomato plant is present in an image, classification is sufficient. The model would label the image as containing a tomato plant or not.
- Object Detection: If you need to pinpoint where the tomato plants are - perhaps for a robot that picks tomatoes - you'd want to use object detection. This method places a bounding box around each plant, helping the robot locate them.
- Segmentation: For an even more detailed analysis, such as measuring the exact shape and area of each leaf, semantic segmentation is the best option. This technique creates a pixel-level mask around each leaf, allowing for precise size measurements. Alternatively, traditional vision techniques like thresholding can help distinguish leaves from the background by identifying where each leaf starts and stops.
Each of these methods provides increasing levels of detail, depending on what insights you need from your tomato field.
What is a computer vision platform?
The work of computer vision is done via a sequence of predictable steps, from collecting and labeling data to training models and integrating the trained models into production environments. Computer vision platforms provide tools to enable all these steps, and reduce the technical barriers to entry. A computer vision platform may offer pre-built models or allow users to create custom models tailored to specific use cases.
How a Computer Vision Platform Works
The goal of computer vision is to teach a computer to see and understand the world in the same way a human does. For example, computer vision can help train a robot to pick up a coffee mug by teaching the computer to recognize where the mug is, where the handle is, and how to move the robotic arm to grasp the handle. This process involves feeding the computer data and helping it identify patterns.
A computer vision platform simplifies this process for businesses, helping them create datasets, train models, and deploy solutions to production:
- Define a specific problem - Start by identifying a clear problem you want computer vision to solve. Whether it's monitoring the presence of objects, detecting theft, or counting weeds, it's important to define your problem specifically. Quantitative problems (like counting objects) are often simpler to tackle than qualitative ones (like assessing product damage).
- Collect your images - Use your own images or camera data or public data from Roboflow Universe and free research datasets to find images representative of your problem. So much of computer vision is about the data that you use. It's kind of remarkable that when you're debugging model performance, understanding why you're getting the inferences you are or the quality of performance that you are, is entirely about the data that you're training on. So it's really important as you think about your tooling that you put emphasis on what data you're collecting, what data you're sending for labeling - do you have balanced classes; do you have augmentations that represent the inference conditions?
- Label your data - Collaborate with your team to identify which images should be sent to labeling, and annotate your images to provide ground truth. Pre-process your images - standard things that you need to do such as re-sizing or increasing contrast. Augment your data to increase your dataset size and representation to improve your model's ability to generalize.
- Train a model: This step involves selecting a model and then testing how it performs through inference (the process of making predictions with the model). When comparing different models, consider the following factors: how quickly the model trains, how fast it performs inference, and how well it performs overall. Additionally, evaluate the model's size (storage requirements) and deployment options. Do you need real-time object detection, or is some delay acceptable, making a server-side deployment more suitable?
- Deploy the model: Deploy to the cloud, on-device, or offline at the edge, and then monitor its performance over time. Keep collecting image or video data from the environment where the model is used, so you can continuously improve and refine the model as needed.
The highest leverage thing you can do is get an initial model to production quickly that solves one part of the problem - for example, it identifies one class (you can usually get a first version ready with a couple hundred well-labeled images). Create fail-safes for when the model doesn't produce an inference that you want, and make it really easy to collect additional data so you can continue to refine the model.
Why Is Computer Vision Important?
Computer vision is becoming a critical tool for businesses to drive efficiency, reduce costs, and enable automation at an unprecedented scale. Businesses can apply it to solve a wide range of challenges, from improving quality control in manufacturing to enhancing the customer experience in retail, and serving more patients with higher quality care in healthcare.
For industries that rely on physical inspection, such as logistics, computer vision helps detect defects, track inventory, and optimize workflows, reducing waste and increasing productivity. In healthcare, it assists in medical imaging analysis, helping doctors detect diseases earlier and with greater accuracy for research. Retailers use it for self-checkout systems, shelf monitoring, and even personalized shopping experiences. In security, it powers real-time threat detection and access control.
Computer vision also provides businesses with valuable insights. By analyzing visual data at scale, companies can make data-driven decisions, improve forecasting, and uncover inefficiencies that might have gone unnoticed. The applications are vast, and as AI models become more capable and accessible, more companies will figure out how to best use them to grow.
The Future of Computer Vision Technology
As models become more performant and efficient, their size is becoming less of a constraint, enabling real-time processing with higher quality. At the same time, compute power is getting smaller and more powerful, allowing larger models to run faster than ever before.
Edge AI is accelerating this shift by deploying machine learning models directly to hardware devices, such as GPUs, where data is processed locally and in real time. Additionally, cameras are becoming more advanced, delivering higher-resolution visual data at lower costs.
These advancements are converging to unlock new use cases that were previously impossible, all while driving costs down. With AI models improving in accuracy and efficiency, the amount of data needed to train them is decreasing, making it easier to bootstrap into new domains. This rapid evolution is paving the way for broad-scale augmented reality applications, and a future where the potential of computer vision is virtually limitless.
How Companies Can Use Computer Vision
Businesses of all sizes - not just tech giants - can leverage the power of vision AI. To leverage computer vision businesses can build a custom solution from scratch or use an existing computer vision platform, including open-source components. Each approach has its own advantages and trade-offs, depending on the company's expertise, resources, and specific needs.
Build your own computer vision platform
Building a custom computer vision solution provides complete control over the model, data pipeline, and deployment process. This is ideal for companies with highly specialized requirements that need to fine-tun models for maximum accuracy, integrate with proprietary systems, and maintain full ownership of their data. However, this approach requires a skilled machine learning and data engineering team, as well as significant time and resources for data collection, model training, optimization, and ongoing maintenance.
Unless you need to do core machine learning research and development, it's usually faster, cheaper, and more effective to spend your time and resources on your domain problem instead of re-inventing the wheel by building your own tooling. As an example, the total cost of ownership of an in-house pipeline is typically over 10x higher than the cost of Roboflow once you account for salaries, infrastructure, and maintenance.
Use a computer vision platform
Alternatively, you can use a computer vision platform to accelerate development and deployment. These platforms provide pre-built tools for image labeling, model training, and deployment, often with user-friendly interfaces that don’t require deep machine learning expertise.
Some platforms offer cloud-based solutions, while others support on-device or edge deployment, allowing companies to choose the best option for their needs. Many enterprise-grade platforms also provide ongoing monitoring and model retraining, ensuring consistent performance over time.
Open-source computer vision platform
For companies that prefer flexibility without the overhead of building from scratch, open-source computer vision platforms, such as Roboflow offer a compelling middle ground. Tools like OpenCV, Detectron2, and YOLO provide powerful vision models and libraries that businesses can customize to their needs.
Open-source solutions benefit from frequent updates, and tend to be a more cost-effective option. However, they still require technical expertise to implement, optimize, and scale effectively, which is why Roboflow provides field engineering partners.
Using a Computer Vision Platform
It is rare for a computer vision model to perform exactly as you expect after you have labeled your first batch of images. It is much more common for each version of your model to become a foundation on which to build future versions. Then, when you have a model that performs to your expectations, you can deploy it to production. Here are some tips that you can use to improve the performance of your vision models.
Once you have a trained model, you will have access to many metrics on how your model performs. These include:
- Precision;
- Recall, and;
- Mean Average Precision (mAP).
You can use these metrics to understand how your model performs.
Precision is a measure of, "when your model guesses how often does it guess correctly?" Recall is a measure of "has your model guessed every time that it should have guessed?"
mAP is equal to the average of the Average Precision metric across all classes in a model. You can use mAP to compare both different models on the same task and different versions of the same model.
Generally, the closer precision, recall, and mAP are to 100%, the better. But these metrics only give you an aggregate view of how your model performs given your validation data.
If you are going to deploy your model in an environment that looks different to the data you trained your model on – for example, if a model will be run in a darker environment than your input data – the metrics may be good but the production performance may be poor.
This is why it is important to both read and understand the metrics, and test your model with production-like data.
Furthermore, computer vision models are rarely “done”. While most of the work may be done on a project during the development phase, it is important to keep your model up to date as the environment in which your model runs changes. Active learning involves collecting production data over time as your model is running.
When you have a trained model version, you can use active learning to:
- Collect images to use in training, and;
- Use the predictions from your existing model version as annotations.
You can then review the predictions and add them to your dataset for use in training a future model version.
You can set up active learning in Roboflow Workflows, our web-based vision application builder. The Roboflow Dataset Upload block lets you selectively add images back to your dataset after your model has run on an input image. You can trigger this to happen conditionally (i.e. save every 100th image).
To learn how to add active learning to your project, refer to our active learning with Workflows guide.
Computer Vision Platform Examples
Here are some popular products for end-to-end computer vision workflows.
- Google Cloud Vision AI: This platform provides pre-trained models and customizable tools for image recognition, object detection, and OCR (optical character recognition). Google Cloud Vision AI is highly scalable and can be integrated with other Google Cloud services for various business applications, from image analysis to content moderation.
- Microsoft Azure Computer Vision: Azure’s end-to-end MLOps platform offers a variety of services, including object detection, image tagging, and text extraction from images. It also features a custom vision model that allows businesses to train their own models for specific use cases, such as detecting certain objects or anomalies in images.
- Roboflow: Roboflow is an open-source computer vision platform that is inter-operable with other tools - it can work with both Google Cloud Vision AI and Microsoft Azure. Roboflow simplifies the process of building and deploying custom models, and offers tools for AI-assisted image annotation, dataset management, training models for tasks like classification, object detection, and segmentation, building workflows, and deploying. Roboflow is popular for its ease of use and support for edge deployment.
Best Computer Vision Platform
As you look to implement your computer vision solution, here are some key considerations to make your selection. The best platforms are:
- Are interoperable with other tools - empowering you to import existing annotations, train your own models, etc.
- Include the latest advancements in the field. For example, you can already use YOLOv12 in Roboflow.
- Deployment agnostic - you should be able to use your models in the cloud, on the web, or on device, based on the nuances of your particular project.
- Include excellent support, including engineering support, to ensure your project is successful.
Roboflow is free to get started and easy to scale up. We’re SOC 2 Type II compliant, support custom security rules, and power customers operating at global scale with terabytes of data today. Learn more about how Roboflow Enterprise can bring computer vision solutions to your business. Talk to an AI expert to discuss your unique use cases.
Keep deepening your knowledge about computer vision: Read computer vision courses.
Cite this Post
Use the following entry to cite this post in your research:
Trevor Lynn. (Mar 4, 2025). What Is A Computer Vision Platform?. Roboflow Blog: https://blog.roboflow.com/computer-vision-platform/