Computer Vision

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As a Chief Operating Officer, you want to introduce efficiencies into the operational processes, applying modern technology to improve safety and quality of work.
As a Director of Quality Control, you want to organise your processes so that you can spot damaged goods in the warehouse, identify substandard or visually incorrect components and track objects on video.
As a Safety Manager, you want to improve the accuracy of operations, detect potential hazards such as fires, gas leaks or unauthorised access in time and respond in real time. You want to have the tools to monitor the safe distance between people and equipment to prevent injury.


Computer vision extends across various sectors, transforming workflows and unlocking new opportunities. Nowadays the technology is applied in a variety of sectors:


Computer vision systems are imperfect and can make mistakes, especially in complex or non-standard situations. Errors can have serious consequences, especially in areas such as health, transport or security. Implementing computer vision systems may seem like a complex task. We as an experienced team is a key factor in the successful implementation of CV systems. We can anticipate potential problems and develop a plan to solve them. Thanks to our knowledge and skills, our team can avoid common mistakes and pitfalls, saving time and budget. The most common risks you may encounter when applying computer vision solutions are:
    Insufficient image clarity:
    Blur, noise, low contrast, or poor lighting can cause the computer vision system to misinterpret images.

    Solution: Using noise reduction, contrast, and lighting algorithms will improve image clarity. We also use infrared or 3D imagery to obtain additional information about an object, and train the algorithm based on typical image problems in a particular environment.
    Insufficient training base:
    A small or diverse training dataset can lead to bias or inability of the system to recognize new objects.

    Solution: The solution is to collect and annotate more data covering different conditions and scenarios. If there is not enough data, we synthetically generate data for expansion, using knowledge gained from other computer vision tasks to speed up training.
    Difficulties in adapting to an "unfamiliar" environment:
    A system trained in a laboratory environment may not work properly in the real world due to different lighting conditions, noise, and other factors.

    Solution: To solve this problem, we place and test it in a real environment.
    The appearance of new objects:
    The system may not recognize new objects that it has not encountered during training.

    Solution: We add new data about emerging objects to expand the system's knowledge, to create systems that can be easily adapted to new tasks.
    Obstacles within the line of sight:
    Obstacles such as smoke, dust, or curtains can prevent the system from seeing objects in the desired range.

    Solution: We additionally use stereo cameras to obtain 3D information about objects and their surroundings. Also we create algorithms that can distinguish objects from obstacles.


We provide a variety of computer vision solutions that can significantly improve your business. Our portfolio includes both artificial intelligence (AI)-based CVs and those using classical algorithms:

01. Traditional Algorithms

We use various image processing techniques such as filtering, segmentation, and morphological operations to extract useful features from images; pattern recognition algorithms such as support vectors and neural networks to classify objects in images; and machine learning algorithms such as decision trees and k-nearest neighbors to make predictions and decisions.

02. Off-the-shelf Convergent Neural Networks

In our projects, we use ResNet - this neural network is a highly regarded solution for image classification, face recognition, and other computer vision tasks. We also use InceptionV3 for high accuracy image classification and SSD for object detection in images.

03. Neural Networks Trained for a Specific Task

We can train a neural network to detect defects on products such as scratches, dents, and cracks, teach it to recognize license plates on cars, or segment images to highlight specific objects or areas.

We are ready to adapt to your unique requirements and provide the optimal solution to meet your needs:


Object Detection Accelerator

Reduce your software development costs by up to 40%! Our object detection accelerator uses advanced artificial neural network and computer vision algorithms to detect objects in video/photo. Detect and identify physical or image-based objects in real time using your device’s camera. Detected objects will automatically be categorized. This video demonstrates the accelerator's proven features.


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Computools shapes the future with products, services and solutions that businesses need to Unlock Tomorrow. We are a global company that delivers on the world-changing ideas that we co-create together with our clients.

Whether we’re designing customer experiences, dealing with our clients or managing our teams, we lead with empathy. Technology is for everyone, not just the geeks. That’s why we focus on using clear, intelligible language, treating every stakeholder with respect and providing support at all stages of development. By starting with a shared understanding of what makes us all human, we’re able to create the favorable conditions necessary to develop our best-in-class solutions.