Computer Vision: What is it and how can it be used in business? What advantages can it bring about? In this article, we aim to provide an overview of this topic, illustrating the potential of this technology.
Computer vision, or artificial vision, is a field of AI that studies algorithms and techniques to enable computers to extract information from images, videos, and other types of data. In essence, this includes all systems capable of analyzing images and videos, going beyond mere image capture to active processing, allowing the interpretation of their content and extraction of information useful for the purposes of the final application.
By using these techniques in a manufacturing context, for example, it is possible to easily, quickly, and accurately inspect the quality of hundreds or thousands of pieces per minute, providing more reliable feedback than the human eye could achieve.
In the following paragraphs, we aim to provide a practical guide on computer vision, identifying its possible applications in practice, the tools to use, and finally, the future perspectives to understand the long-term evolution.
How to Use Computer Vision: Practical Applications
There are several possible practical applications where computer vision can be employed to achieve multiple benefits. Here are some examples:
- Food production: Computer vision can be used to detect possible contaminations in products to ensure consumer safety.
- Risk or emergency situations: By monitoring people’s movements in public spaces, computer vision solutions like ai-Guard can be employed to verify compliance with Covid-19 safety measures through monitoring.
- Predictive maintenance: By monitoring the state of machinery and equipment, it is possible to prevent failures or problems.
- Retail sector for enhancing customer experience: Analyzing facial expressions and body movements allows understanding customer reactions to products and services, enabling personalized offerings.
Tools and Techniques for Successfully Implementing Computer Vision
To implement computer vision solutions, various tools and libraries are available to facilitate the development and implementation of artificial vision algorithms. Here are some of the main tools used in this field:
- OpenCV (Open Source Computer Vision Library): A popular library for computer vision, offering a wide range of functions for image manipulation, object recognition, object tracking, camera calibration, and other artificial vision operations.
- TensorFlow: An open-source machine learning library developed by Google, widely used for creating and training neural networks, including models for computer vision.
- PyTorch: Another popular machine learning library known for its dynamic and flexible interface in creating neural networks.
- Keras: A high-level library that works on TensorFlow, Theano, and Microsoft Cognitive Toolkit, simplifying the creation and training of neural networks.
- Scikit-learn: Primarily a library for traditional machine learning, Scikit-learn also offers algorithms and tools for artificial vision, such as pattern recognition and feature extraction.
- Dlib: A C++ library with Python bindings known for its effectiveness in facial image processing, face recognition, and object tracking.
- YOLO (You Only Look Once): A framework for object detection known for its speed and accuracy, suitable for real-time applications.
- CUDA and cuDNN: To accelerate training and execution of computer vision models on NVIDIA GPUs.
Among the vertical platforms, Revelis offers the Colibri quality control solution, which uses neural networks to implement computer vision techniques. Colibri’s features include:
automatic analysis of industrial product images, detection of basic components, identification of component positions, and verification of the presence or absence of necessary components in the final product.
Colibri aims to provide quality control in industrial settings, offering benefits such as automatic product analysis, comprehensive batch analysis, and assurance of higher compliance and production quality.
By benefiting from these factors, companies can achieve significant advantages, including quality controls, increased production efficiency, reduced labor costs, and continuous process tracking.
Future Perspectives: Evolution of Computer Vision
Computer vision is increasingly pervasive today, and speed, quality, and cost savings are crucial factors for the success of any organization. In the near future, the following trends need to be considered:
- Development of unsupervised and semi-supervised learning techniques: Reducing the need for large amounts of labeled data for training;
- New European regulations: Placing more emphasis on privacy and security;
- Emergence of ethical development guidelines and the spread of AI technologies.
In the coming years, the prevalence of computer vision will increase, and organizations will increasingly need to master this technology. At the same time, it is crucial to rely on reliable suppliers capable of supporting the company through every step, from implementing solutions to managing the most innovative solutions and the necessary expertise.
Revelis has a team of specialists with extensive experience in AI solutions, offering companies computer vision solutions that meet industrial compliance and quality requirements. Revelis also provides all the necessary support for solution implementation and management. Contact us to discover how to optimize the quality control process with our AI solutions.
Harness the full potential of AI solutions, contact us to discover the most suitable solution for optimizing every process in your business.
