Template: OAK-D Spec
From Waveshare Wiki
Revision as of 08:13, 15 March 2022 by Waveshare-eng11 (talk | contribs)
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
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
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