The Most Powerful Embedded Platforms for Computer Vision
Computer vision, a field that enables computers to interpret and understand visual information from the world, requires devices with the right balance of power, performance, and efficiency. As of August 2023, several powerful embedded platforms and single-board computers (SBCs) stand out for their capabilities in driving advanced computer vision applications. This article explores the most notable options for those looking to build high-performance systems tailored to computer vision tasks.
Top Embedded Platforms for Computer Vision
1. NVIDIA Jetson Series
NVIDIA Jetson Nano: Designed for entry-level and mid-range applications, the Jetson Nano is an affordable and compact device that supports CUDA for efficient parallel processing. This makes it ideal for projects that require some level of computational power without breaking the bank.
NVIDIA Jetson TX2: For those looking for better performance, especially for real-time applications, the TX2 is a more powerful option than the Nano. It supports deep learning frameworks and is well-suited for applications requiring live processing of video and images.
NVIDIA Jetson Xavier NX: Providing a compact and powerful solution, the Xavier NX is perfect for advanced AI and computer vision tasks. Its high-performance GPU and ARM processors make it a standout choice for complex and resource-intensive applications.
NVIDIA Jetson Orin: As the latest addition to the Jetson family, the Orin offers significant improvements in processing power and efficiency. It is an excellent choice for complex and demanding computer vision applications, providing the balance required for both real-time and high-resolution tasks.
Low-Cost SBCs for Computer Vision
2. Raspberry Pi with Accessories
The Raspberry Pi, particularly the Raspberry Pi 4, is a popular choice due to its affordability and extensive community support. While not as powerful as the NVIDIA Jetson series, the Raspberry Pi can handle basic computer vision tasks with the addition of a camera module and libraries like OpenCV. This makes it an ideal choice for hobbyists and enthusiasts who want to start with computer vision without a large investment.
AI-Oriented Embedded Solutions
3. Google Coral
The Coral Dev Board is specially designed for AI inference at the edge, combining an Edge TPU (Tensor Processing Unit) for optimized machine learning performance. It is particularly suitable for AI applications, including computer vision, with the added benefit of low power consumption, making it ideal for applications requiring long battery life.
Powerful Mini-PCs for Demanding Applications
4. Intel NUC
Intel NUC (Next Unit of Computing) are compact and powerful mini-PCs capable of running full versions of Linux or Windows. They are ideal for applications that require high processing power and memory, such as advanced computer vision tasks. These devices offer a good balance between performance and cost, making them a versatile choice for both hobbyists and professionals.
AI and Vision-Focused Platforms
5. BeagleBone AI
Combining the power of a BeagleBone Black with advanced AI capabilities, the BeagleBone AI features dedicated processors for machine learning and computer vision. Its ability to handle AI tasks efficiently makes it a strong contender for projects that integrate both AI and computer vision.
High-Performance FPGA-Based Platforms
6. Odroid Series
Models like the Odroid N2 offer robust performance and support various Linux distributions. They are capable of running computer vision frameworks and handling demanding tasks. For highly specialized and performance-critical applications, FPGA-based platforms from companies like Xilinx or Intel can be utilized, providing parallel processing and hardware acceleration capabilities.
Considerations for Selection
1. Performance Needs: Assess the complexity of computer vision tasks, such as real-time processing or deep learning requirements.
2. Power Consumption: Consider the power requirements of your application, especially for battery-operated devices.
3. Ecosystem and Community Support: A strong community provides libraries, tools, and troubleshooting assistance, which can be crucial for project success.
4. Cost: Budget constraints can significantly influence the choice of platform, as costs can vary widely depending on specific performance requirements.
Summary: For robust computer vision applications, the NVIDIA Jetson series is often the go-to choice due to its balance of power and support for deep learning. However, depending on specific project needs, alternatives like Google Coral, Intel NUC, or even Raspberry Pi can be effective solutions. Choosing the right embedded platform will depend on your unique requirements, ensuring a successful deployment of your computer vision project.