Equipped with high-performance quad-core AI intelligent vision processor with computing power up to 2.0Tops, the module supports 4K video coding and decoding, various AI frameworks and human detection with high recognition accuracy — it is suitable for face recognition, gate access control, gesture recognition, expression recognition, face attribute analysis, etc. Abundant resources facilitate secondary development and help the project to be implemented quickly.
Low-consumption AI vision processor RV1126, with 14nm lithography process and quad-core 32-bit ARM Cortex-A7 architecture, integrates NEON and FPU — the frequency is up to 1.5GHz. It supports FastBoot, TrustZone technology and multiple crypto engines.
Built-in neural network processor NPU with computing power up to 2.0 Tops realizes that the power consumption of AI computing is less than 10% of the power required by the GPU. With tools and supporting AI algorithms provided, it supports direct conversion and deployment of Tensorflow, PyTorch, Caffe, MxNet, DarkNet, ONNX, etc.
With built-in 3F-HDR ISP, multi-level noise reduction, 3F-HDR, and other technologies, it is equipped with dual-lens 2M (RGB+IR) WDR camera; it not only meets the scenes of strong light, backlight and darknessVarious AI frameworks, but also realizes human detection and anti-spoofing functions of face recognition.
It supports face database up to 100,000 capacity; it has efficient recognition speed, and identifies the target quickly — the recognition accuracy rate is 99.7%.
Built-in Video CODEC supports 4K H.254/H.265@30FPS and multi-channel video encoding and decoding, meeting the needs of low bit rate, low-latency encoding, perceptual encoding and making the video occupancy smaller.
It can be used as a USB device to output visual processing data and images to the computer; or can be used as an IPCAM monitoring and identification front end to connect to the NVR through the network port; and also can be connected to a 1080P screen through MIPI to make a face recognition or an AI vision terminal.
Equipped with all-aluminum alloy shell, the space-saving module has efficient heat dissipation and can be flexibly embedded in various AI products.
A complete SDK, including cross compiler toolchain, BSP source code, application development environment, development documents, examples and other resources, is provided for the users to make a further customization.
It is widely used in face recognition, gesture recognition, gate access control, smart security, smart doorbell/peephole, self-service terminals, smart finance, smart construction, smart travel and other industries.
CAM-C1109S2U | CAM-C1126S2U | |
SoC |
RV1109 |
RV1126 |
CPU |
Dual-core ARM Cortex-A7 32-bit , frequency 1.5GHz, integrate NEON and FPU Each core has a 32KB I-cache, 32KB D-cache and 512KB shared second-level cache Based on RISC-V MCU |
Quad-core ARM Cortex-A7 32-bit, frequency 1.5GHz, integrate NEON and FPU Each core has a 32KB I-cache, 32KB D-cache and 512KB shared second-level cache Based on RISC-V MCU |
NPU |
1.2Tops, support INT8/ INT16, It has strong network model compatibility and provides RKNN tool, which can realize the conversion of commonly used AI framework models Eg: (caffe, darknet, mxnet, onnx, pytorch, tensorflow, tflite) and algorithm support |
Computing power up to 2.0Tops, supports INT8/ INT16, It has strong network model compatibility and provides RKNN tool, which can realize the conversion of commonly used AI framework models Eg: (caffe, darknet, mxnet, onnx, pytorch, tensorflow, tflite) and algorithm support |
RAM |
1GB / 2GB DDR3 |
1GB / 2GB DDR4 |
Storage |
8GB / 16GB eMMC |
8GB / 16GB eMMC |
Video Encoding |
H.264/H.265 encoding capability: 2688 x 1520@30 fps+1280 x 720@30 fps 3072 x 1728@30 fps+1280 x 720@30 fps 2688 x 1944@30 fps+1280 x 720@30fps |
4K H.264/H.265 30fps video encoding: 3840 x 2160@30 fps+720p@30 fps encoding |
Video Decoding |
5M H.264/H.265 decoding |
4K H.264/H.265 30fps video decoding 3840 x 2160@30 + 3840 x 2160@30 fps decoding |
OS |
Linux |
Linux |
Power |
5V |
5V |
Operating Temperature |
-10℃~60℃ |
-10℃~60℃ |
Operating Humidity |
10%~90 % |
10%~90 % |
Dual-Lens Camera | ||
Camera (IR) | Camera (RGB) | |
Dual-Lens Camera |
GC2053 |
GC2093 |
Sensor Size |
1 / 2.9 |
1 / 2.9 |
Resolution |
1920*1080 |
1920*1080 |
Pixel Size |
2.8 μm |
2.8 μm |
Output Format |
RAW |
RAW |
Interface |
MIPI |
MIPI |
Focus Distance |
80 cm |
80 cm |
Lens |
4P |
4P |
Filter |
850 nm |
650 nm |
Field Angle |
D70°H62°V38° |
D70°H62°V38° |
Distortion |
≤0.5% |
≤0.5% |
Aperture/ Focal Length |
F2.0/4.3mm |
F2.0/4.3mm |
Resolving Power |
Center: 800 Around: 600 |
Center: 800 Around: 600 |
Face Recognition & Detection | ||
Max Database |
100,000 |
|
Recommended Database |
10,000 |
|
Accuracy |
Under standard test environment, 10,000 database No Mask: Accuracy 99% With mask: Accuracy 95% |
|
Face Detection |
Face detection time: ~23ms Face tracking time: ~7ms |
|
Human Detection |
Single-lens detection time: ~45ms Dual-lens detection time: ~15ms |
|
Face Recognition |
Feature extraction time: ~25ms Single recognition time: ~0.0115ms |
|
Recommended Image Input Size |
720P |
|
Min Face Recognition Size |
50*50 pixel (Non-human) 90*90 pixel (Human) |
|
Recommended Face Recognition Angle |
Yaw ≤ ±30° Pitch ≤ ±30° Roll≤ ±30° |
|
Appearance | ||
Dimension |
84.0mm × 22.45mm × 19.35mm |
|
Shell |
All aluminum alloy design, efficient heat dissipation |