Ai Algorithms Camera
Support Add Ai Algorithms to Camera Module
You can add AI algorithms to security camera modules—especially those based on Rockchip RV1126—through a combination of hardware and software integration. Here’s how support for AI algorithm addition is achieved and what it entails:
How AI Algorithms Are Supported on Camera Modules
On-Device AI Acceleration: The RV1126 features a high-performance quad-core ARM Cortex-A7 CPU and an integrated Neural Processing Unit (NPU) with up to 2.0 TOPS, designed for efficient real-time AI inference. This hardware accelerates demanding tasks such as object detection, facial recognition, and scene analysis, supporting AI algorithms directly on the device without needing constant cloud connectivity.
AI Framework Compatibility: Modules based on RV1126 support direct conversion and deployment of models built with major AI frameworks like TensorFlow, PyTorch, Caffe, MXNet, Darknet, and ONNX. This allows you to develop or refine your AI algorithm on a PC and then convert, quantize, and deploy it onto the camera platform, leveraging the NPU for real-time analytics.
SDK & Development Tools: Manufacturers provide comprehensive software development kits (SDKs) and tools to speed up algorithm deployment. These tools typically include model conversion utilities, routines for pre-compilation and board-level deployment, and APIs for managing and invoking AI inference tasks.
Custom AI Models: The camera module’s AI ecosystem is flexible—allowing you to add or customize AI models for tasks such as automated event detection, face or license plate recognition, object tracking, or behavioral analytics. Edge AI capabilities enable running even complex neural networks locally on the camera, reducing bandwidth use and latency.
Typical Workflow to Add AI Algorithms
Develop or select an AI model using frameworks such as TensorFlow or PyTorch.
Convert the model to a format compatible with the NPU (using provided tools).
Pre-compile or quantize the model if necessary (often required for INT8/INT16 execution).
Deploy the model to the camera (via the SDK) and integrate with the camera’s operating system and APIs.
Configure real-time inference to execute AI tasks (recognition, detection, etc.) on live video streams.
Example Camera Solutions
The Horus AI Camera (RV1126-based) offers direct support to accelerate the development and deployment of custom AI algorithms for surveillance, facial recognition, and video analytics.
Benefits
Real-time, local execution of advanced AI analytics (face, object detection, etc.)
Lower bandwidth and cloud dependency due to on-device inference.
Simple upgrade path for existing systems—new algorithms/models can be flashed via firmware updates.
In summary: AI camera modules like those using the Rockchip RV1126 are designed to easily support and add custom AI algorithms, thanks to robust NPU hardware, multi-framework compatibility, and comprehensive development tools. This empowers integrators and developers to deploy sophisticated vision-based analytics as their security or smart city requirementsrements evolve
Get Test & Measurement Technologies & Solutions!
Specializing in FPGA-based Control, Processing, and Scceleration technologies
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Source-Measurement Unit (SMU)
• Combines the functions of five instruments (digital multimeter, voltage source, current source, electronic load, and pulse generator) into one unit.
• Typical output sources with 0.02% measurement accuracy, supporting both DC and pulse output modes.
• Pulse output mode with a minimum pulse width of 100 us and a rise time of 10 us.
• High-precision source with pA-level resolution and support for multi-channel parallel current expansion.
• Supports 2-wire and 4-wire resistance measurements.
• Compatible with SCPI driver and commands.
Data Logger
• Resolution: 32 Bits
• Sampling Rate: 250 KSPS
• Recording dual-channel voltage inputs simultaneously and continuously
• Uploading raw data to the PC in real-time
BMU Tester
• Precision grade 0.01%
• Support DC voltage, current, nA level standby current, resistance and other routine tests
• Support over/under-voltage, over-shoot/discharge temperature, short-circuit and other protection tests
• Communication bus levels are programmable and compatible with a wide range of Gas Gauge ICs
• Support SWD or llC firmware burn-in
• Fast testing speed, supporting up to 24 channels of parallel testing
• Powerful software with friendly user interfaces, which can be uploaded to the MES system and used as a test device in R&D or production
3.75 GHz RF Acquisition and Playback Module
• Main Controller: XCKU040-2FFVA1156I chip form Xilinx UltraScale series
• RF Performance: Supports dual channels, with 2-lane TX channels supporting 10MHz to 6GHz frequency output; 2-lane RX high-speed adjustable data channels offering frequency input from 10MHz to 8GHz
• Extensive Expansion Interfaces: 2 HPC FMC expansion interfaces, 2 Gigabit Ethernet interfaces, 2 JTAG download and debugging interfaces and 1 M.2 socket interface
• ADC Performance: 14-bit resolution, 3 GSPS sampling rate
• DAC Performance: 16-bit resolution, 12.6 GSPS sampling rate
• Customization: offer customization services for the hardware interfaces and functions of the FPGA development board, along with ADC and DAC submodule
Archon-Automated Test Manager
- Powerful automated test development and management application software, which can quickly develop, deploy, and manage automated test and verification systems.
- Archon can be used to develop, execute, and deploy test cases and test plans, and extend the functionality of the system by developing plug-ins on Archon.
- Archon provides extensible plug-ins for report generation, database recording, and connections with other enterprise systems.
Zynq7000 Series
• Powered by: XilinxXC7Z020 / 010 / 007S
• Modular Design: enables flexible integration
• Compact Build: allows seamless embedment
• High Performance: FPGA + ARM + High-speed I/O
• Comprehensive FPGA IP core library
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