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Oak-1 OpenCV 相機套件 (深度人工智慧) (B款，12MP 固定對焦IMX378)
〔Oak-1〕 分3個版本：
Ａ．AF自動對焦IMX378 12MP
Ｂ．FF固定對焦IMX378 12MP
Ｃ．FF固定對焦OV9782 1MP
For all scenarios where depth perception is not requisite, OAK-1 offers processing power of RVC2 core in a smallest possible body. 對於不需要深度感知的所有場景，OAK-1 在盡可能小的機身中提供 RVC2 核心的處理能力。
影片１：https://youtu.be/mPbN5JzaVrQ
影片２：https://youtu.be/JbVkNUfiPrE
- For 12 MP central RGB camera auto-focus and fixed-focus variants are available; there is also 1 MP OV9782 sensor based option
- 4 TOPS of processing power (1.4 TOPS for AI)
- Dimensions: 36x54.5x27.8 mm; Weight: 53.1g
- USB2 / USB3 for power delivery and communication
【清單】：
- OAK-1 Camera 攝像頭
- Cable USB-CA-1m 線
- Cleaning cloth
OAK-1 is an 12MP AI camera that features on-device Neural Network inferencing and Computer Vision capabilities. It can capture high-resolution images, run custom AI models, and perform advanced computer vision tasks. It uses USB-C for both power and USB3 connectivity.
OAK-1是一款 12MP AI 相機，具有裝置上的神經網路推理和電腦視覺功能。它可以捕獲高解析度圖像、運行自訂人工智慧模型並執行高級電腦視覺任務。它使用 USB-C 進行電源和 USB3 連接。
- 4 TOPS of processing power (1.4 TOPS for AI - RVC2 NN Performance)
- Run any AI model, even custom architectured/built ones (models need to be converted)
- Encoding: H.264, H.265, MJPEG - 4K/30FPS, 1080P/60FPS
- Computer vision: warp (undistortion), resize, crop via ImageManip node, edge detection, feature tracking. You can also run custom CV functions
- Object tracking: 2D tracking with ObjectTracker node
- Dimension: 36x54.5x27.8 mm
- Weight: 53.1g
The Oak-1 is an all-in-one machine vision solution. It’s a 4-trillion-operations-per-second AI powerhouse that performs your AI models on-board, so that your host is free to do whatever you need it to do.
Oak-1 是一款一體化機器視覺解決方案。它是一個每秒執行 4 萬億次操作的 AI 引擎，可在機上執行您的 AI 模型，以便您的主機可以自由地執行您需要它執行的任何操作。
Its integrated 12 MegaPixel camera module communicates over an on-board 2.1 Gbps MIPI interface directly to the Myriad X, which ingests this data and performs neural inference on it, returning the results over USB.
Such a data path offloads the host processor from all of this work. In the common use case of object detection from a 12MP image, this means your host is now dealing with a 24 Kbps stream of what the objects are and where they are in the image, instead of a 2.1 Gbps stream of video. So an 87,500 reduction in data your host has to deal with.
And such a reduction means that even on relatively-slow hosts, one can use dozens of Luxonis OAK-1 without burdening the host CPU.
As an example, below is an example of running MobileNet-SSD:
作為示例，下面是運行 MobileNet-SSD 的示例：
OAK-1 + Raspberry Pi: 50+FPS, 0% RPi CPU Utilization
NCS2 + Raspberry Pi: 8FPS, 225% CPU Utilization
