Embedded Vision Technologies
This guide provides an overview of embedded vision platforms, development tools, and implementation strategies for building intelligent vision applications.
Introduction
Embedded vision systems integrate image capture and processing capabilities directly into devices, enabling real-time visual intelligence in applications ranging from autonomous vehicles to industrial automation. This documentation covers key platforms, development approaches, and optimization techniques for building embedded vision solutions.
Supported Platforms
NVIDIA Jetson
Edge AI computing platform designed for autonomous machines and embedded systems.
| Feature | Specification |
|---|---|
| GPU | NVIDIA Ampere/Volta Architecture |
| AI Performance | Up to 275 TOPS |
| Power Efficiency | 10-30W power envelope |
Learn more about Jetson development →
NVIDIA DRIVE AGX Orin
Scalable platform for autonomous vehicle development with advanced AI capabilities.
| Feature | Specification |
|---|---|
| Performance | Up to 254 TOPS |
| Processing | 12-core ARM Cortex-A78AE CPU |
| Safety | ASIL-D functional safety |
Qualcomm Platform
Mobile-optimized vision processing solutions leveraging Qualcomm's heterogeneous computing architecture.
| Feature | Specification |
|---|---|
| AI Engine | Hexagon DSP with Tensor Accelerator |
| Vision Processing | Adreno GPU + ISP |
| Integration | Complete mobile SoC solution |
Implementation Strategies
- Hardware Acceleration: Leveraging dedicated vision processing units (VPUs), GPUs and AI accelerators
- Software Optimization: Techniques for efficient algorithm implementation and memory management
- Pipeline Design: Creating efficient capture-to-inference processing chains with minimal latency
- Power Management: Balancing processing capabilities with energy constraints for battery-powered devices
Development Workflow
- Platform Selection: Choose hardware based on performance, power, and feature requirements
- Camera Integration: Configure appropriate camera interfaces (MIPI CSI-2, USB, etc.)
- Vision Framework Setup: Implement OpenCV, CUDA Vision, or platform-specific vision libraries
- Algorithm Deployment: Optimize neural networks and vision algorithms for the target hardware
- Testing & Optimization: Benchmark and refine for latency, throughput, and power consumption
Common Applications
- Autonomous vehicles and advanced driver assistance systems (ADAS)
- Industrial automation and visual inspection
- Smart city infrastructure and traffic monitoring
- Robotics and drone navigation systems
- Security and surveillance
- Consumer electronics and augmented reality
Platform Comparison
| Platform | AI Performance | Power Efficiency | Integration Ease | Ecosystem |
|---|---|---|---|---|
| NVIDIA Jetson | ★★★★★ | ★★★☆☆ | ★★★★☆ | ★★★★★ |
| NVIDIA Drive AGX | ★★★★★ | ★★☆☆☆ | ★★★☆☆ | ★★★★☆ |
| Qualcomm | ★★★★☆ | ★★★★★ | ★★★★☆ | ★★★★☆ |