Computer vision is an AI field that uses digital images and deep learning to enable machines to interpret and understand the visual world for automation in various domains.
How do we do it?
Computer vision is a field of artificial intelligence that enables machines to interpret and understand the visual world. It uses digital images from cameras, videos, and deep learning models to identify and classify objects. This technology allows systems to accurately automate tasks that require visual cognition, which is critical in fields like autonomous driving, medical imaging, or industrial automation.
We developed in-house methods and processes that allow us to quickly preprocess your data and prototype various models. We preconfigure, train them in object classification and pattern recognition, and validate to compare their precision and efficiency. That process allows us to quickly prototype solutions that can be further developed and integrated into larger systems later in the process.
At xBerry, we go beyond basic detection by creating advanced computer vision solutions tailored to each client's specific needs. Our expertise in computer vision software empowers us to build systems capable of real-time decision-making, enabling higher levels of automation and operational intelligence. By combining our experience in AI and deep learning, we deliver computer vision services that help businesses enhance quality control, improve safety, and optimize production workflows. Our holistic approach ensures that every project is scalable, efficient, and ready to support future growth, making us a trusted partner among computer vision companies worldwide.
Where is it used?
Computer vision is revolutionizing industries by enabling machines to see, understand, and interact with the world around them. Its applications go far beyond simple image recognition, empowering businesses to automate complex processes, improve safety, and enhance quality at every stage of production. Today, computer vision solutions are widely used in manufacturing, where they support real-time quality inspection, detect defects, and ensure consistent standards on assembly lines. This type of industrial computer vision reduces human error and helps companies maintain high production efficiency while minimizing waste.
In logistics and warehousing, computer vision services play a crucial role in automating inventory management, tracking packages, and optimizing space utilization. Smart cameras combined with advanced computer vision software allow for instant identification and classification of goods, improving speed and accuracy. Retailers, on the other hand, use computer vision solutions for automated checkout systems, customer behavior analysis, and personalized marketing, creating more engaging shopping experiences.
Healthcare is another field where computer vision companies are making a significant impact. Automated diagnostic tools help analyze medical images, detect anomalies, and support early disease detection, providing doctors with faster and more accurate insights. In agriculture, computer vision AI companies develop systems that monitor crop health, automate harvesting, and analyze growth patterns, enabling more sustainable and productive farming.
The integration of computer vision IoT is pushing the boundaries even further, allowing for real-time data collection and analysis directly from connected devices and sensors. This combination enables smart cities to manage traffic flow, monitor public safety, and optimize energy usage more effectively. In security and surveillance, computer vision software enhances threat detection, enables facial recognition, and improves overall situational awareness, providing organizations with a higher level of protection and control.
Another exciting application is in robotics and autonomous vehicles, where SLAM technology (Simultaneous Localization and Mapping) allows machines to navigate complex environments with precision. This advancement supports not only self-driving cars but also drones and warehouse robots, enabling them to operate autonomously and safely in dynamic settings.
At xBerry, we specialize in designing tailored computer vision solutions that address industry-specific challenges and drive innovation. Our team combines expertise in computer vision software, IoT integration, and advanced AI to deliver reliable, scalable, and future-proof systems. As one of the leading computer vision AI companies, we help organizations transform their operations, enhance efficiency, and gain a competitive edge. Whether you're exploring automated quality control, smart surveillance, or advanced robotics, our comprehensive computer vision services can turn your vision into reality and empower you to lead in your market.
Process
Requirement Analysis
Understand the needs of the client. Identify what kind of objects or patterns the system should be able to recognize, and the level of accuracy needed. It’s essential to analyze the operational environment, lighting conditions, and data quality to ensure optimal performance of computer vision solutions.
Data Collection
Gather large amounts of image or video data relevant to the task. This data might need to be labeled, either manually or through automated means. Proper labeling is crucial to train accurate and reliable computer vision models that can distinguish subtle differences between objects and patterns.
Preprocessing
Clean and standardize the data to make it suitable for model training. This step involves removing noise, correcting inconsistencies, and ensuring that images or videos are in a uniform format. Proper data preparation is essential for creating robust computer vision solutions that perform accurately in diverse environments.
Model Selection and Training
Choose a suitable machine learning or deep learning model based on the task. Train the model on the preprocessed data, tuning the model parameters to optimize its performance. Selecting the right architecture is critical for building effective computer vision solutions capable of handling complex visual tasks. During training, parameters are adjusted to improve accuracy and reduce false detections, ensuring the model performs well in real-world applications.
Validation and Testing
Validate the model using a separate subset of the data to check its performance. Test the model in conditions as close as possible to the final application. This step ensures the computer vision solutions are robust, reliable, and ready for real-world deployment. By using realistic scenarios and varied data, we can identify potential weaknesses and fine-tune the model for maximum accuracy.
Deployment
Integrate the computer vision system into the client's existing IT infrastructure. This could involve deploying the model to a server, an edge device, or a cloud-based platform. Seamless integration ensures that the computer vision solutions work smoothly with other business systems, enabling real-time data exchange and automated workflows. Choosing the right deployment method depends on performance requirements, latency needs, and security considerations.
Monitoring and Maintenance
Continually monitor the system's performance in the real-world scenario. Regular maintenance might involve retraining the model with new data, adjusting parameters, or updating the software as required. Ongoing monitoring ensures that computer vision solutions remain accurate and relevant as conditions or data patterns change.
Feedback and Iteration
Gather feedback from the client and end-users. Use this feedback to make iterative improvements to the system, enhancing accuracy and usability. Listening to real-world experiences helps identify unexpected challenges, uncover new opportunities, and ensure that the computer vision solutions truly meet business needs. Continuous updates and refinements keep the computer vision software aligned with changing operational goals, maintaining high performance and reinforcing the value of your computer vision services over time.
Case study
Crossing Guard
Crossing Guard is an innovative AI solution for safe rail crossing. Already in use at a level crossing in Poland, it effectively tracks traffic flow and enhances safety.
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.
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