Computer Vision Development Services

THE VISION

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 is a niche area of technology that, when applied correctly, can help your organisation in solving these challenges. It helps facilitate the creation of smart factories that can automatically perform tasks normally done by humans. Advanced computer vision technologies allow us to identify objects e.g. workers in a warehouse, product defects, shell craters; analyse data from surveillance cameras and use it for safeguarding or navigation. Autonomous robot control, 3D mapping, augmented reality are quickly becoming the reality. Companies that use this technology in their operations can safely declare themselves to be part of Industry 4.0 – the highest degree of interoperability between industry and technology.

COMPUTER VISION BENEFITS

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

1. Healthcare

Computer vision can help doctors diagnose diseases by analysing medical images such as X-rays and MRIs. It can be used to study molecules and biological processes, or to perform surgical procedures with minimally invasive techniques.

2. Security

Vision technology can be used to recognise people's faces, monitor property, and analyse data from surveillance cameras to detect suspicious behaviour.

3. Retail

Computer vision can be used to automate checkouts, analyse customer behaviour, and personalise advertising for customers.

4. Agriculture

In the agriculture industry, computer vision can be used to monitor the health of crops, detect pests and diseases at an early stage, and automate harvesting.

5. Energy

It can be used to inspect infrastructure such as power lines and wind turbines, monitor mining operations, and predict energy production from renewable sources.

6. Transport

CV is a key technology for the development of autonomous vehicles. It can be used for traffic control and vehicle inspection. For example, Tesla's Autonomous Driving System uses computer vision to navigate roads, helping to improve safety and convenience.

RISKS AND SOLUTIONS

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. An experienced team is a key factor in the successful implementation of CV systems. It can anticipate potential problems and develop a plan to solve them. Thanks to their knowledge and skills, the 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. You can 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, it is necessary to 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, it is necessary to place and test in a real environment.

The appearance of new objects:

The system may not recognize new objects that it has not encountered during training.

Solution: It is necessary to 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: It is necessary to additionally use stereo cameras to obtain 3D information about objects and their surroundings. Another solution is to create algorithms that can distinguish objects from obstacles.

COMPUTOOLS OFFER

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:

Traditional algorithms

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.

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.

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:

01. Text & Data Recognition

You can benefit from advanced text and data recognition solutions that greatly simplify information processing. We use advanced libraries, such as Tesseract, PaddleOCR, EasyOCR, that provide highly accurate extraction of text data from a variety of sources, automating and optimising your business. For example, you will be able to automate medical record processing, digitize historical documents or automate invoice processing, etc.

02. Objects Recognition

You can benefit from using advanced object recognition technologies that enable your system to see and understand the world around you. Object recognition solutions provide high accuracy and speed in identifying different objects in images or video streams. This can be used to automate inventory, security, monitoring and other areas where it is important to recognise and classify objects quickly and accurately, like real-time anomaly detection in manufacturing, autonomous security robots or smart inventory management.

03. Objects Composition & Comparison

You can take advantage of the tools to compare objects, allowing you to analyse the structure and properties of objects to make more informed decisions. You can compare objects based on various parameters such as shape, size, texture, and other characteristics. This is useful in tasks such as product quality, image comparison or data analysis, providing you with valuable insights for decision-making.

04. Image classification

You benefit from customised image classification solutions that automatically identify and group images based on pre-defined criteria. This approach includes the use of advanced machine learning algorithms and neural networks for accurate and fast classification, for example, content moderation on social media platforms, personalized product recommendations or automatic image tagging.