Computer vision is a field of Artificial intelligence that enables computers to analyse, interpret, and understand images and videos in a manner similar to human visual perception. These technologies can automatically recognise objects, track movement, analyse visual features, and draw conclusions from visual data. Computer vision is widely used in many industries, such as automotive, medicine, surveillance, robotics, and retail.
Computer Vision
Type of technology
Description of the technology
Basic elements
- Image recognition algorithms: Systems that identify objects, faces, colours, and other visual features.
- Artificial neural networks: Networks used in computer vision, such as convolutional neural networks (CNNs), that analyse images.
- Image processing: Techniques for filtering, segmentation, edge detection, and analysis of image structures.
- Motion tracking algorithms: Methods for identifying and tracking object motion in video sequences.
- Deep learning systems: AI models that learn to recognise patterns in visual data from large data sets.
Industry usage
- Automotive industry: Obstacle recognition and navigation in autonomous vehicles.
- Medicine: Automatic analysis of medical images, such as X-rays or MRIs, to diagnose diseases.
- Retail: Systems that monitor customer behaviour in shops to optimise sales.
- Industry: Automatic quality control of products on production lines.
- Surveillance: Monitoring of public spaces, identification of people and objects, and analysis of events.
Importance for the economy
Computer vision is revolutionising many industries, including manufacturing, automotive, medicine, and commerce. Automatic object recognition, quality control in factories, diagnosing diseases from medical images, and managing surveillance systems are just some of the applications that are changing the way companies operate. With computer vision, companies can reduce costs, increase efficiency, and improve the quality of their products and services.
Related technologies
Mechanism of action
- Computer vision is based on converting images or video into digital data that can be processed by Artificial intelligence algorithms. In the initial phase, images are segmented and analysed to detect objects, object features, or movement. Neural networks, such as CNNs, process this data to recognise patterns and compare them to previously learned models. Based on the results of the analysis, the systems can make decisions, such as identifying objects, tracking movement, or generating scene descriptions.
Advantages
- Process automation: Computer vision enables automation of tasks such as visual inspection, monitoring, and analysis.
- Precise analysis: Computer image analysis systems are more accurate and faster than human eyes.
- Increasing safety: It supports surveillance, identification, and real-time monitoring systems.
- Cost reduction: It reduces the need for manual monitoring or image processing.
- New opportunities: Computer vision enables the creation of new products and services, such as autonomous vehicles and intelligent management systems.
Disadvantages
- Privacy: Surveillance systems based on computer vision can violate users’ privacy.
- Technological complexity: Implementing advanced computer vision systems requires specialised knowledge and resources.
- Dependence on data quality: Computer vision systems can fail if images are of poor quality or training data is limited.
- High implementation costs: Implementing advanced computer vision–based solutions can be costly.
- Recognition errors: Despite technological advances, the systems can make mistakes, e.g. in identifying objects.
Implementation of the technology
Required resources
- Image data sets: Large amounts of images needed to train computer vision models.
- IT infrastructure: Servers for processing images and training AI models.
- Software: Tools for image analysis and implementation of computer visual analysis models.
- Team of specialists: Computer vision experts, AI engineers, and image processing specialists.
- Computing environment: Distributed processing platforms to support complex computing operations.
Required competences
- Image processing: Ability to work with algorithms for image analysis and processing.
- Machine learning: Knowledge of AI models used in computer vision, such as CNN.
- Programming: Programming skills in tools that support image analysis (Python, OpenCV, TensorFlow).
- Model optimisation: Ability to adapt models to specific requirements, such as real-time object recognition.
- IT infrastructure management: Competence in the operation and maintenance of sophisticated image processing systems.
Environmental aspects
- Energy consumption: Analysing large sets of images requires considerable energy resources.
- Emissions of pollutants: Data centres that process visual data contribute to CO2 emissions.
- Raw material consumption: The need for advanced hardware infrastructure may lead to increased demand for scarce resources.
- Recycling: Computer hardware used in computer vision systems requires regular upgrades, which generates electronic waste.
- Water consumption: Data centres needed to support computer vision can contribute to high water consumption in cooling processes.
Legal conditions
- Legislation governing the implementation of solutions, such as AI Act (example: regulations on accountability for decisions made by computer vision–based systems).
- Safety standards: Regulations on the security of data and images processed by computer vision systems (example: ISO/IEC 27001).
- Intellectual property: Protection of copyright related to visual data and image processing algorithms (example: copyright on photos and images).
- Data security: Regulations for the protection of personal data processed by computer vision–based surveillance systems (example: GDPR in the European Union).
- Export regulations: Restrictions on the export of advanced computer vision systems to sanctioned countries (example: AI technology export restrictions).