Tech

What is Embedded Vision and How it’s Shaping the Future of Image Processing

Published on

November 4, 2024

What is Embedded Vision?

Embedded vision refers to the integration of computer vision capabilities - feel free to check out our article on what is computer vision if needed - into compact, embedded systems, such as microcontrollers or system-on-chips (SoCs). Unlike traditional vision systems that rely on general-purpose computers to process images and extract information, embedded vision operates on smaller, dedicated hardware, allowing real-time analysis and decision-making in a wide range of devices, from smartphones and industrial robots to standalone intelligent cameras.

The rise of these embedded systems is driven by advances in artificial intelligence (AI), deep learning, and the miniaturization of powerful computing hardware. This technology is increasingly present in areas where there is a need for automated and power efficient decision-making without the need for bulky or high-cost systems.

Embedded vision is revolutionizing the image processing landscape by enabling smarter, more efficient systems that can operate autonomously in real-time. Traditional image processing systems often rely on large, power-hungry computers to analyze visual data. This limited their deployment to specific environments where space, power, and costs were not constraints. However, with the rise of embedded vision, sophisticated image processing is now possible on compact, low-power devices, bringing advanced visual capabilities to a broader range of applications.

In this article we will discuss how embedded vision works, its key components, different applications of these vision systems, their advantages over traditional systems and what is to come in the future of embedded vision.

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How Does Embedded Vision Work?

Embedded vision systems work by processing image data captured by cameras or sensors and then extracting relevant information to perform a specific task. The core of the system is a vision processing unit (VPU) or an AI processing unit, which is optimized for running deep learning algorithms such as Convolutional Neural Networks (CNN) or Transformers. These processing units are usually optimized to execute algorithms for tasks like object detection, pattern recognition, or motion tracking.

Here's a simplified workflow of how embedded vision typically operates:

‍Image Acquisition: A camera or optical sensor captures visual data in real-time.‍
Preprocessing: The raw image data is processed to enhance quality (e.g., noise reduction, contrast adjustment).‍
Inference: Using machine learning models or other algorithms, the system makes decisions or classifications based on the visual input.‍
Action or Output: The processed data is used to trigger an action, such as activating a robot arm, generating a report, or notifying a user.

Embedded vision systems often utilize machine learning models pre-trained on large datasets and optimized for processing units with limited computational power, allowing them to recognize complex patterns and objects efficiently on low-power devices.

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Key Components of Embedded Vision Systems

Embedded computer vision systems usually come in two main forms: standalone cameras with integrated processing units or separate cameras connected to edge devices or processing boards. For any of these devices main components of the embedded vision system include:

Camera or Image Sensor: Captures the visual data. The sensor resolution and frame rate depend on the application, whether it’s monitoring quality control on a factory line or enabling vision for a drone, an autonomous vehicle or an industrial robot.

Processing Unit (VPU, FPGA, SoC, GPU): Performs the computational tasks needed to analyze and interpret the visual data. VPUs and GPUs are optimized for deep learning models, while FPGAs and SoCs offer flexibility and speed for real-time processing.

Memory (RAM, Flash): Stores both the raw image data and the machine learning models used for inference.

Connectivity: Embedded systems often include wireless (Wi-Fi, Bluetooth) or wired (Ethernet, USB) communication for data transmission or remote control. Whether the processing unit is directly connected to the camera or not is a key factor regarding connectivity. All in one devices (standalone cameras) can provide high speed connectivity between camera and processing unit while separate systems are limited by the bandwidth of the camera connection. Most common protocols for camera connectivity are USB3.0, GigE, 2.5GigE, 10GigE and GMSL.

Power Supply: Since embedded systems are often attached to robots or vehicles, power efficiency is a critical consideration. Many of these vision systems are designed to run on low power as 5V or 12V and are often battery-operated.

Software and Algorithms: Software frameworks like OpenCV, TensorFlow Lite, TensorRT, ONNX, or specialized AI models enable feature extraction and decision-making processes. Usually manufacturers provide software for translating or compiling models from these mainstream frameworks to their proprietary processing unit platforms.

Standalone devices incorporate all key components into one device which usually only needs external power. One mainstream manufacturer of embedded cameras is FLIR with their Firefly DL line. Most commonly, embedded computer vision is implemented using external machine vision cameras that are connected to edge devices with AI capabilities such as those from Nvidia Jetson Lineup, Qualcomm Edge AI boxes or Google Coral boards.

Graph showing both samples of Firefly DL Camera and e-con systems

Applications of Embedded Vision

These systems have a broad range of applications, from consumer electronics to industrial automation. Key areas include:

Autonomous Vehicles: Cameras and vision systems in self-driving cars detect objects, pedestrians, and lane markings to assist with navigation and safety. In Agritech drones and machines can detect crops and plants for automated irrigation or automatic pest control.

Healthcare: Computer vision in edge devices is used in medical devices for imaging, diagnostics, and robotic surgery, allowing real-time analysis of medical data and assisting in decision-making.

Smart Cameras: Security systems and surveillance cameras leverage low power in-place processing to recognize faces, track motion, and detect unusual behaviors without needing external computing resources.

Industrial Automation: In manufacturing, industrial embedded camera devices enable robotic systems to inspect products, monitor processes, and ensure quality control with high precision and high speed enabling faster and more efficient production lines.

Augmented Reality (AR) and Virtual Reality (VR): In mobile and wearable devices computer vision enables AR and VR by interpreting the environment in real time, allowing for more immersive user experiences such as interactive virtual try-ons for cosmetics in mobile devices or augmented information in smart glasses.

Drones and Robotics: Drones equipped with vision processing capabilities can perform tasks such as obstacle avoidance, mapping, and object tracking autonomously and in real time.

Retail and Inventory Management: Local vision systems monitor shelves, track customer behavior, and automate stock counting in retail environments.

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Image from industrial computer vision system from Digital Sense client Sienz for fruit defect detection and sorting.

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Embedded vision vs traditional vision systems

Embedded vision systems offer several advantages over traditional computer vision systems:

	Traditional Computer Vision	Embedded Vision
Processing Units	Image processing and machine learning algorithms analysis are run on external computers or cloud platforms, separate from the camera system.	Computer vision and machine learning occurs directly on the device or in specialized processing devices like Qualcomm AI Boxes, NVIDIA Jetson or Google Coral Devices.
Size	Are typically larger, comprising a camera and a separate full-sized PC for processing.	Are more compact, as processing happens on-device, ideal for space-constrained applications like robots, autonomous vehicles or wearable devices.
Power Consumption	Traditional systems rely on GPUs, which can consume significant power (hundreds of watts per hour) to run deep learning models.	These systems are based on energy-efficient architectures like ARM, running optimized deep learning models with minimal power consumption, usually ranging from a few watts to 50 watts per hour.
Adquisicion costs	Initial building costs are often lower, using off-the-shelf consumer hardware as pre-built PCs or gaming-purposed GPUs and CPUs.	initial acquisition cost, especially for industrial-grade processing units, is higher than traditional computers. Robust computer vision cameras for outdoor applications can also be more expensive than traditional security cameras.
Ease of integration	These are easier to integrate as they rely on mainstream consumer hardware and interfaces which can be easily interconnected with other hardware devices.	Embedded systems require more expertise for integration, especially in industrial applications, as they may need to connect with external devices like PLCs or control systems.
Real Time Processing	Achieving near real-time processing requires powerful hardware as High power GPUs and CPUs. Can achieve real time processing but not in an optimal way.	Are specifically designed for real-time computer vision and decision-making as all computation occurs on-device or on an edge device and machine learning models and components are selected with efficiency in mind.
Flexibility	Are built to be general-purpose, allowing for versatility through configuration and software development. Therefore potentially enabling multiple applications with the same system.	Are typically tailored for a specific task , with the camera sensor, camera lens, processing unit, and machine learning models selected and optimized for the given application.

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Future Trends

The future of embedded vision is promising, with several trends set to drive innovation:

Edge AI: With the rise of edge computing, computer vision systems will increasingly incorporate AI at the device level. Following the trend of IoT which enabled more and more devices to be interconnected, embedded AI will improve everyday devices with AI capabilities.This will enable faster and more secure decision-making in applications like smart cities, smart manufacturing, autonomous vehicles, drones and robots.

More Efficient AI Models: Although there is a trend to train bigger and bigger models in the fields of Large Language Models (LLMs) and foundational vision models, in the case of embedded vision there is a trend to design and implement more efficient deep learning models. As computer vision algorithms become more efficient, on-device vision systems will be able to handle increasingly complex tasks with less computational power, allowing for further miniaturization and lower costs.

Multi-Sensor Fusion: Future embedded systems will integrate data from multiple sensors (e.g., LiDAR, infrared) in addition to cameras, improving their ability to perceive and understand complex environments. At the moment on-device vision systems are usually focused on 2D image and video processing, however, there is a trend to incorporate more information from different sensors such as 3D point clouds from LiDAR or even sound data from microphones to create holistic systems that can analyze the surrounding in a more complete way.

Customizable AI: End-users will have the ability to fine-tune efficient vision models for their specific applications, leveraging tools that simplify model training and deployment, thus democratizing access to advanced vision systems. Even if this is already available by multiple labeling platforms or open source frameworks such as Ultralytics. At the moment these solutions provide minimal code or no-code model training for a limited number of machine learning models. It is expected that this trend will continue enabling more architectures and more efficient models to be trained with minimal coding and minimal machine learning expertise for different devices and applications.

In conclusion, efficient computer vision is transforming industries by bringing intelligent vision capabilities to compact and low-power devices, offering both cost and performance advantages over traditional systems. As AI and hardware technologies advance, embedded vision systems will become an even more critical component of modern technology.

For more insights, check out our success stories on embedded computer vision in Agritech, Industrial Quality Control and Augmented Reality or get in contact with us to explore how we can help apply Computer Vision to solve your problems.