What is Vision AI?

Vision AI is the application of artificial intelligence to process and interpret visual data from the world (I.e. images, videos, and live camera feeds) in real time. It enables machines to see, understand, and act on visual information, transforming ordinary cameras into intelligent sensors.

By combining computer vision techniques with machine learning, Vision AI can identify objects, detect patterns, recognize anomalies, and make decisions instantly, without human intervention. This unlocks new possibilities for automation, safety, and efficiency across industries.

Take the Vision AI with Cumulocity course

Why Vision AI matters

Visual data is one of the richest and most abundant sources of information, but it is traditionally challenging to use at scale. Sending raw video to the cloud for analysis can incur high bandwidth costs, raise privacy concerns, and introduce unacceptable delays.

Vision AI addresses these challenges by moving intelligence closer to where the data is generated; at the “edge”, which allows decisions to be made on-device in milliseconds. This approach enhances responsiveness, reduces infrastructure costs, and protects sensitive information.

With Vision AI, your systems can:

• Detect and classify objects in real time.
• Recognize faces or verify identities securely.
• Spot anomalies like defects, intrusions, or unusual behaviors.
• Read text from labels, screens, or signage.
• Segment images into zones for deeper context.

How Vision AI works

Vision AI systems use deep learning models trained on labelled images or video clips. Once trained, these models can interpret new visual inputs in real time. Typical capabilities include:

• Object detection and classification: identifying and categorising items in a scene.
• Scene segmentation: dividing images into regions for more detailed analysis.
• Text or facial recognition: reading printed text, identifying people, or verifying identities.
• Anomaly detection: spotting defects or abnormal patterns instantly.

Processing can take place entirely on the device (e.g. a smart camera or edge gateway), ensuring low latency and enhanced privacy while minimising the need to transmit large video files.

Previously, Vision AI systems required computational power that was available only on the cloud, requiring the transmission of large video files with sometimes sensitive information. Modern edge platforms, like the NVIDIA Jetson^TM, now allow processing to take place entirely on the device (e.g. a smart camera or edge gateway), ensuring low latency and enhanced privacy while minimising the need to transmit large video files.

Figure 1. Cloud Vision AI.

Figure 2. Edge Vision AI.

Core components of a Vision AI system

• Sensors and devices. At the heart of any Vision AI system is the imaging hardware; from standard IP cameras to intelligent vision sensors that integrate AI processing directly on the chip.
• AI models. Models are trained using large, diverse datasets to recognise relevant features in the visual data. These models can be general-purpose or highly specialised for specific tasks such as quality inspection or safety compliance.
• Processing infrastructure. Edge processing enables near-instant analysis, while cloud resources can be used for model training, large-scale data storage, and fleet-wide updates.
• Application logic and integration. Outputs from Vision AI systems can trigger automated responses, inform human operators, or integrate with business systems such as ERP, MES, or asset management platforms.

Benefits of Edge Vision AI

• Faster decision-making. Act in milliseconds rather than waiting for cloud processing.
• Lower operational costs. Reduce bandwidth usage and avoid expensive data transfers.
• Improved privacy. Analyse on-device and share only relevant metadata.
• Greater flexibility. Deploy in remote or bandwidth-constrained locations.
• Scalability. Manage hundreds or thousands of devices across operations.

Challenges of Edge Vision AI

• Data quality and diversity. Training models require well-labelled and representative datasets.
• Model deployment and updates. Distributing AI models across diverse hardware environments can be complex.
• Hardware selection. Choosing devices that can handle required processing workloads.
• Integration with existing systems. Ensuring outputs can be acted upon in operational workflows.

What can Vision AI deliver that traditional sensors can’t?

Vision AI turns visual data into meaningful, immediate action across industries.

Manufacturing

• PPE compliance monitoring – Detect workers missing required safety gear
• Defect detection – Flag surface flaws in real time
• Dangerous behavior alerts – Spot unsafe actions like entering restricted zones
• Machine status checks – Remotely read warning indicators or lights
• Forklift safety zones – Trigger alerts when pedestrians enter high-risk areas

Healthcare

• Hand hygiene compliance – Detect sanitization events before patient contact
• Fall detection – Send instant alerts for abnormal patient movement
• Mask usage monitoring – Ensure adherence in sterile areas
• Crowding alerts – Prevent overcrowding in waiting rooms
• Visitor time tracking – Support infection control policies

Utilities

• Substation intrusion detection – Flag unauthorized entry
• Smoke/fire detection – Early warning for critical assets
• Leak confirmation – Visually validate suspected water or gas leaks
• Vegetation encroachment – Identify tree growth near power lines
• Protective gear verification – Ensure worker safety compliance

Transport & Logistics

• Load condition monitoring – Detect damage during loading/unloading
• Dock congestion alerts – Optimize truck and forklift traffic flow
• Pallet count verification – Automate inventory movement checks
• Restricted zone entry – Flag vehicles in unsafe areas
• Manual handling safety – Identify poor posture to prevent injuries

Retail

• Empty shelf detection – Trigger restocking alerts automatically
• Customer dwell time analysis – Understand shopper engagement
• Checkout queue length monitoring – Adjust staffing dynamically
• Planogram compliance – Verify correct product placement
• Suspicious behavior alerts – Flag potential theft risks

Smart Cities

• Traffic flow monitoring – Optimize signals and reduce congestion
• Public safety alerts – Detect unusual crowd movement or incidents
• Parking space management – Identify open spots in real time
• Environmental monitoring – Spot flooding, littering, or hazards
• Energy optimization – Adjust lighting or HVAC based on occupancy patterns

Emerging trends in Vision AI

• On-sensor AI. Sensors like the Sony’s IMX500 combine imaging and AI processing on a single chip.
• No-code model creation. Tools now enable non-experts to label, train, and deploy models quickly.
• Federated learning. Models learn locally on devices, sharing updates without exposing sensitive data.
• Multi-modal AI. Combining visual data with sensor readings, text, or audio for richer context.

Getting started with Vision AI

Organisations exploring Vision AI should:

1. Identify high-impact use cases such as defect detection, safety monitoring, or process optimisation.
2. Select suitable imaging hardware and deployment environments.
3. Ensure access to high-quality training data for model creation.
4. Choose a platform that supports device management, model deployment, and integration at scale. Platforms such as Cumulocity IoT integrate device orchestration, AI model deployment, and application enablement into a single environment, allowing enterprises to connect, train, and scale Vision AI solutions efficiently.