Embedded Vision Systems: Key Components, Benefits, and Real-World Applications for Smarter Automation
Gaspar van Elmbt
In the fast-changing world of industrial automation and smart technology, embedded vision systems are transforming how machines perceive and respond to the world around them. Unlike traditional vision setups that rely on external processing units, embedded vision enables compact devices to capture, process, and analyze images locally at the edge leading to faster response times, increased efficiency, and smarter decision-making.
We provide the high-performance components that make embedded vision systems work: industrial cameras, precision lenses, and advanced lighting solutions. This article explains what embedded vision is, its core components, showcases its applications, and highlights why it’s becoming essential to modern machine systems.
Table of contents
What Is Embedded Vision and How Does It Work?
Embedded vision refers to the integration of image capture and processing directly into compact, intelligent devices. In simple terms, it means that a device like a robot, camera, or even a smart appliance can “see” understand, and react to its environment all on its own hardware.
Unlike traditional vision systems that send images to a separate computer for processing, embedded vision systems handle everything internally. This makes them faster, more efficient, and ideal for applications where size, power, and speed are important
Key Components of an Embedded Vision System
Vision Sensors (Cameras):
- Capture visual data from the environment providing the raw images needed for embedded vision applications. Common interfaces include USB, Ethernet, MIPI, etc.
- ARM-based PCs also use USB or Ethernet cameras. We have tested our solutions with small development boards like NVIDIA Jetson and Raspberry Pi which are widely supported for embedded vision.
- MIPI (Mobile Industry Processor Interface) is suitable for lower resolution applications, though it can support higher resolutions depending on the implementation.
- Cameras range from compact modules to those with built in autofocus functions like the 2MP 10x AFZ MIPI Camera Raspberry Pi.
Processing Units (Embedded PCs):
- Act as the brain of the system, analyzing captured images and making decisions.
- Ranging from entry-level options like Raspberry Pi to advanced platforms such as, NVIDIA Xavier, and Orin.
- The challenge lies in the need to balance processing power, cost, and energy efficiency, especially for high-resolution or multi-camera setups.
- While any processor can be theoretically used, the most promising for embedded vision would be high-performance embedded CPUs, application-specific standard products, Graphics Processing Units (GPUs) with CPUs, DSPs with accelerators, mobile application processors, and FPGAs.
Algorithms and Software:
- Transform raw images into useful information.
- Vision applications typically involve a pipelined sequence of algorithms for:
- Image Quality Improvement: Fix lens distortions, enhance contrast, and stabilize images.
- Object Information Extraction: Identify objects using edge, motion, color, or size-based techniques (e.g., optical flow for motion, pedestrian detection).
- Inference and Decision-Making: Analyze and classify objects (e.g., distinguish between vehicles, pedestrians, or road signs). - Requires significant processing power as complex tasks can require billions of calculations per second.
- Our dedicated software solution, Daheng Galaxy SDK, provides certified drivers, programming interfaces, and comprehensive tools for image acquisition, processing, and analysis. This makes it easy to integrate and control your VA Imaging hardware within your embedded vision systems.
- For even more flexibility, you can leverag open-source libraries and platforms such as OpenCV, which offers thousands of pre-built algorithms for a wide range of vision tasks.
- For Windows-based development, Zebra Aurora Vision Studio offers a graphical, drag-and-drop environment with a comprehensive set of image analysis filters, enabling rapid prototyping and deployment of machine vision applications – no coding required
Lenses and Lighting:
- Machine Vision Lenses determine the field of
view, depth of field, and image clarity, directly affecting the quality of raw data. - Machine Vision Lighting ensures consistent and optimal illumination, which is essential for accurate image processing and reliable detection
Why Embedded Vision Is Growing in Popularity?
The virtuous circle of technological advancement where more capable processors enable higher volume applications, which in turn drive further chip developments rapidly accelerating the adoption of embedded vision. Key advantages of Embedded Vision include:
Common Embedded Vision Applications
Embedded vision is already revolutionizing multiple industries. Here are just a few real-world examples.
Partnering for Your Embedded Vision Needs
As embedded vision continues to reshape automation, the need for high-quality and well-integrated components becomes more critical. That’s where VA Imaging comes in.
Our strengths:
- Industrial Cameras: Whether you need a compact camera module for a custom design or a high-speed camera with specific interfaces, choosing the correct camera is the first step toward building a successful embedded vision system.
- Precision Lenses & Lighting: Our team helps you select the best lenses and lighting to ensure your system captures the precise data needed for accurate processing and top performance.
- Expert Guidance: Developing embedded vision systems requires knowledge in both computer vision and embedded hardware. Our team can help bridge this gap by advising on components.
Our team supports you in selecting the right components for your system, ensuring smooth integration, optimum performance, and scalable design that grow with your needs.
Are you designing or upgrading an embedded vision system? Contact VA Imaging today to discuss your project and get expert advice on selecting the right cameras, lenses, and lighting to bring your application to life.
