The Impact of AI on Vision-Based Edge Devices

Vision-based AI edge devices

Imagine applications powered by AI vision, processing data instantly, securely and right at the edge. That's the revolution of vision-based edge applications and it's changing the game for every industry.

The integration of artificial intelligence (AI) into vision-based edge devices has a significant impact on various areas. In this blog post, we will discuss the benefits and use cases of this innovative technology.

Benefits of AI on Vision-Based Edge Devices

  • Real-time response
    AI on edge devices analyses visual data locally, enabling faster detection, pattern recognition, and decision-making. This is crucial in applications where low latency is required, such as autonomous vehicles and industrial automation.
  • Increased efficiency
    Edge AI reduces the amount of data that needs to be sent to the cloud, which saves bandwidth and lowers network costs. This optimizes performance and reduces operational costs.
  • Enhanced privacy and security
    By processing data locally, sensitive images are not shared with the cloud, which improves privacy and security. This is crucial in applications where confidentiality is important, such as surveillance and medical imaging.
  • Offline functionality
    Edge AI makes it possible to use vision-based devices without an internet connection. This is ideal for applications in remote areas or environments with unreliable connectivity.

Use Cases

Military, security and surveillance

The modern battlefield is a complex, data-driven environment, demanding agile, informed decisions in real-time. Creating enhanced situational awareness is a top priority in the battlefield and in surveillance.

Edge devices with AI integration can collect and analyse data from multiple sensors (drones, cameras, radar) in real-time, providing a comprehensive picture of the battlefield. AI-based vision devices can also create better autonomous systems, resolving issues that normally need human interference.


AI can detect obstacles, pedestrians and other vehicles in real-time, enabling more reliable autonomous navigation for autonomous vehicles. Whether in autonomous cars, trains or other modes of autonomous transport, AI-Based vision application can make transportation safer and more reliable.

Industrial automation

Edge AI can be used for quality control, product inspection and robotic tasks in manufacturing environments. Lightning-fast image analysis can prevent accidents, as well as manufacturing faults that might lead to product recalls.


AI can be used for analysis of medical images, such as X-rays and MRI scans. AI can aid in the diagnoses and plan treatments for patients. Offloading the workload from medical professionals and creating better treatment plans.

Unveil the future

The integration of AI into vision-based edge devices has a tremendous impact. The benefits of real-time response, increased efficiency, enhanced privacy and offline functionality make this technology attractive for a wide range of applications. We expect the development of AI on edge devices to continue to grow in the coming years, with even more innovative use cases that will transform society.

Curious about what AI-based Edge Devices can do for your organisation?

See our live demo at:

what AI can do on edge devices

Develop your own AI embedded vision application

Our demo kit is based on the NXP i.MX 8M Plus. This powerful application processor consists of, among other things, 4 Cortex-A53 cores, a Cortex-M7 MPU, a DSP and GPU. This makes it ideal for machine learning, vision, advanced multimedia and IIoT applications. With this demo kit, we show how easy it is to develop your own vision AI application.

By using a compact industrial computer module with a large variety of ready-made AI models integrated on it, a Raspberry PI camera and a 10-inch touchscreen, you can get started quickly. The pre-configured AI models can be easily extended with standard machine learning tools.

For more information and prices, please contact us.


Here are some additional details about the demo kit:

  • Processor: NXP i.MX 8M Plus
  • Cores: 4 Cortex-A53, 1 Cortex-M7
  • Memory: 4GB LPDDR4
  • Storage: 64GB eMMC
  • Connectivity: Gigabit Ethernet, Wi-Fi, Bluetooth
  • Display: 10-inch touchscreen
  • Camera: Raspberry PI camera
  • Software: Linux, Yocto Project
  • AI Models: Object detection, classification, segmentation, tracking

The demo kit is a great way to get started with AI embedded vision development. It provides everything you need to create your own custom applications, including a powerful processor, a variety of sensors, and pre-configured AI models.

Gilles Hendrikx

Gilles Hendrikx

Build Acceleration | Boards | UI | Development

Expert on vision-based AI edge

Contact me