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Overview
OAK-D and the OAK-D-PoE is an MIT-licensed open-source software and Myriad X-based hardware solution for computer vision at any scale. which is made by the OpenCV team.
The OAK-D baseboard has three onboard cameras which implement stereo and RGB vision, piped directly into the DepthAI SoM for depth and AI processing. The data is then output to a host via USB 3.1 Gen1 (Type-C).
The OAK-D-PoE baseboard offers full 802.3af, Class 3 PoE compliance with 1000BASE-T speeds. The OAK-D-POE baseboard has three onboard cameras which implement stereo and RGB vision, piped directly into the DepthAI SoM for depth and AI processing. The data is then output to a host via 1000BASE-T ethernet connection.
Features
- Depth measuring range: 0.2 ~ 9m
- Depth camera: Global shutter 120fps / 200fps
- AI chip: Intel Myriad X 4TOPS computing performance
- Video plug flow: 4K 30 fps H.265 plug flow
- Connector: Gigabit Ethernet, with 802.3af-compliant PoE circuit
- Expansion interfaces: GPIO, SPI, UART
- NN platform support: all platforms
- Power consumption: 2W ~ 5.5W
- Development language: Python, C++
- Eclosure: Aluminum enclosure
- Weight: 361g
Camera Specifications
OAK-D/OAK-D-PoE
| Camera |
Color camera |
Monochrome camera
|
| Shutter
|
Rolling
|
Globar
|
| Sensor
|
IMX378
|
OV9282
|
| Max framerate
|
60fps
|
120fps
|
| H.265 framerate
|
30fps
|
/
|
| Resolution
|
12MP (4056×3040 px/ 1.55um)
|
1MP (1280×800 px/3um)
|
| FoV
|
81° DFoV – 68.8° HFoV
|
81° DFoV – 71.8° HFoV
|
| Lens size
|
1/2.3 Inch
|
1/2.3 Inch
|
| Focus
|
8cm – ∞ (AutoFocus)
|
19.6cm – ∞ (FixedFocus)
|
| D-number
|
2.0
|
2.0
|
Camera Specifications
OAK-D-Lite
| Camera |
Color camera |
Monochrome camera
|
| Shutter
|
Rolling
|
Globar
|
| Sensor
|
IMX214
|
OV7251
|
| Max framerate
|
60fps
|
200fps
|
| H.265 framerate
|
30fps
|
/
|
| Resolution
|
48MP (4208×3120 px)
|
0.3MP (640×480 px/3um)
|
| FoV
|
81.3° DFoV
|
85.6° DFoV
|
| Lens size
|
1/3.1 Inch
|
1/7.5 Inch
|
| Focus
|
8cm
|
6.5cm
|
| D-number
|
2.2
|
2.2
|
Supported NN
- Caffe*
- AlexNet
- CaffeNet
- GoogleNet (Inception) v1, v2, v4
- VGG family (VGG16, VGG19)
- SqueezeNet v1.0, v1.1
- ResNet v1 family (18***, 50, 101, 152)
- MobileNet (mobilenet-v1-1.0-224, mobilenet-v2)
- Inception ResNet v2
- DenseNet family (121,161,169,201)
- SSD-300, SSD-512, SSD-MobileNet, SSD-GoogleNet, SSD-SqueezeNet
- TensorFlow*
- AlexNet
- Inception v1, v2, v3, v4
- Inception ResNet v2
- MobileNet v1, v2
- ResNet v1 family (50, 101, 152)
- ResNet v2 family (50, 101, 152)
- SqueezeNet v1.0, v1.1
- VGG family (VGG16, VGG19)
- Yolo family (yolo-v2, yolo-v3, tiny-yolo-v1, tiny-yolo-v2, tiny-yolo-v3)
- faster_rcnn_inception_v2, faster_rcnn_resnet101
- ssd_mobilenet_v1
- DeepLab-v3+
- MXNet*
- AlexNet and CaffeNet
- DenseNet family (121,161,169,201)
- SqueezeNet v1.1
- MobileNet v1, v2
- NiN
- ResNet v1 (101, 152)
- ResNet v2 (101)
- SqueezeNet v1.1
- VGG family (VGG16, VGG19)
- SSD-Inception-v3, SSD-MobileNet, SSD-ResNet-50, SSD-300
