Computer vision is a field of AI that enables machines to interpret and analyze visual data from images and video.
Where is computer vision used?
Computer vision is used across multiple industries to automate visual tasks and improve efficiency. In manufacturing, it is used for quality inspection, defect detection, and monitoring production processes. In logistics and warehousing, computer vision enables package tracking, inventory management, and automation of sorting systems.
In retail, it is used for automated checkouts, customer behavior analysis, and product recognition. In healthcare, computer vision supports medical imaging, anomaly detection, and diagnostics.
In agriculture, it is used to monitor crops, detect diseases, and optimize farming processes. In robotics and autonomous systems, computer vision enables navigation, object recognition, and interaction with the environment. These applications allow companies to reduce errors, improve accuracy, and automate complex processes.
How do we build computer vision systems?
We build computer vision systems using a structured process that includes data preparation, model development, and deployment. The process starts with collecting and preprocessing visual data, such as images or video from production environments. Next, we train machine learning models for tasks such as defect detection, object recognition, or quality inspection, and validate their performance in real-world conditions.
Based on this, we develop an MVP to quickly test the solution and then scale it into a production-ready system integrated with existing infrastructure. This approach ensures that computer vision systems are accurate, reliable, and ready to operate in industrial environments.
Computer Vision Implementation Process
Discovery and feasibility analysis
We define the business goal, identify objects or patterns to detect, and assess technical constraints such as camera setup, environment, and required accuracy.
Output: project scope, feasibility assessment, recommended approach.
Data collection and preparation
We collect and label image or video data, then clean, normalize, and augment it to ensure high-quality training data.
Output: structured and production-ready dataset.
Model development and training
We select the appropriate model architecture (e.g. detection, segmentation, OCR) and train it on prepared dat, optimizing for accuracy, speed, and deployment constraints.
Output: trained and optimized model.
Validation and real-world testing
We evaluate the model on unseen data and test it in real conditions, measuring metrics such as precision, recall, and latency.
Output: validated model with defined performance benchmarks.
Deployment and integration
We deploy the model to cloud, on-premise, or edge environments and integrate it with existing systems, workflows, or devices.
Output: production-ready computer vision system.
Monitoring and continuous improvement
We monitor performance, retrain models with new data, and optimize the system as conditions or business needs evolve.
Output: long-term accuracy, stability, and scalability.
Case study
SpaceOS
SpaceOS is a Mixed Reality System designed for our Swedish partner. SpaceOS enables users to interact with interfaces and objects by performing hand gestures.
Vaccine Cold Chain
Our solution utilizes RFID tags and QR codes on vaccine containers, enabling fully monitored transport processes. Successfully tested by a leading European vaccine manufacturer.
xBerry Talks – New Opportunities in the Virtual World – VR in Business with Badal Dixit and Cobalt Cox
Industry 4.0. Smart Machines and Automation
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