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.

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.

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.

Emerging trends in Vision AI

• On-sensor AI. Sensors like the Sony 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.