CAM-C1109S2U Overview Specifications Tutorial Technical Case Download
CAM-C1126S2U Overview Specifications Tutorial Technical Case Download

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CAM-C1109S2U CAM-C1126S2U

CAM-C1126S2U Intelligent Dual-Lens Camera Module

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.

Quad-core AI vision processor

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.

Various AI frameworks

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.

HDR+ WDR dual-lens

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.

Efficient recognition, high accuracy

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%.

4K H.265 encoding & decoding

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.

Master & slave integration

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.

Small size, easy to integrate

Equipped with all-aluminum alloy shell, the space-saving module has efficient heat dissipation and can be flexibly embedded in various AI products.

Abundant resources for customization

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.

Applications

It is widely used in face recognition, gesture recognition, gate access control, smart security, smart IP camera, smart doorbell/peephole, self-service terminals, smart finance, smart construction, smart travel and other industries.

face recognition
gesture recognition
gate access control
self-service terminals
smart finance
smart construction

Specification

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
存储

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