Computer Vision

AI solutions for crystal clear vision

Our AI solutions process images and video, recognize objects and scenes, and generate descriptions and labels for visual content. In other words, thanks to computer vision, you never lose sight of your business value.

Object detection

We develop custom, high-performance machine learning models for object detection, even in challenging real-world circumstances. We tailor our approach - data pre-processing, modeling, tuning and setup - to fit each unique use case for optimal results.

Video analysis

Unlock the full potential of your video data with our expert object tracking techniques. We help you detect and segment objects, identify activities, and overcome the unique challenges of video analysis, like resource management and model architecture.

Edge vision

Edge Processing of video can reduce network traffic and increase data security. With our Edge Video Analysis (EVA) project, subsidized by Vlaio, we aim to develop a high-performing solution for anonymizing and re-identifying video on the edge.

Visual inspection

Monitor, steer and improve your quality control with advanced machine vision technology. Our algorithms detect various defects, optimizing production processes.

Client cases

Discover how our expertise in Computer vision leads businesses to success

Typical challenges

Building a computer vision solution can be complex process. Let's take a look at some common challenges.

Data collection & diversity

Computer vision algorithms need labeled data to learn, but getting this data can be difficult and expensive. That’s why it’s important to use the data effectively, which means getting good quality data and making sure it covers a wide range of situations. This leads to more accurate and robust computer vision models.

Object detection, segmentation and tracking

Finding and separating objects in an image is important for many computer vision tasks. It can be challenging to track objects over time, especially if they're hidden or have complicated shapes, but there are ways to overcome these challenges and improve the accuracy of computer vision models.

Image classification and recognition

Computer vision aims to accurately and efficiently identify objects in images, even if some objects are hard to tell apart. With proper training on large datasets, computer vision models can achieve high accuracy and improve our ability to analyze visual information.

Model selection

Choosing the right model is crucial for successful computer vision solutions. It requires a deep understanding of algorithms and fine-tuning for specific use cases. While transfer learning can be useful, adapting pre-trained models to new domains is important for optimal results.


Computer vision applications often need to process data quickly, but the algorithms can be demanding. This means that it's important to find ways to make the algorithms faster without losing accuracy. Finding the right balance between speed and accuracy is key.

Interpretability and explainability

Explaining how computer vision models work can be tough because they often use complex neural networks. But there are ways to make these networks more interpretable, so stakeholders can understand and use the models more effectively.

High level outline of the solution

Data acquisition

To start building a computer vision solution, you first need to collect and prepare data. This includes cleaning and annotating images or videos, and gathering a diverse dataset that represents all possible cases.

Model architecture

Convolutional Neural Networks (CNNs) and Transformer-based algorithms are commonly used models for computer vision. These models are designed to detect and analyze features and patterns in images using specific architectures.


For the computer vision model to work, it must be trained using the collected data. This process includes minimizing the error between predicted and actual results, which requires powerful hardware, such as GPUs.


After the model is trained, it is evaluated to see how well it performs using various evaluation metrics such as precision, recall, mIoU and mAP.


Once trained and evaluated, the model can be used to perform tasks such as image classification, object detection, and image segmentation. Depending on the resources and infrastructure available, deployment can be on-premise or in the cloud.

Incremental training and adaptation

When new data becomes available or the model's application changes, it may need to be fine-tuned or adapted. Retraining the model on new data or updating its parameters can be part of this process.

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Get in touch to discover how our computer vision solutions can transform your business operations

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