• Embedded Box-PC supporting Nvidia’s Jetson TX2

  • Suitable for deep learning inference and other applications requiring a GPU accelerator

  • Supports vision relevant interfaces

  • Compact, low power consumption, long-term availability

Several applications require an accelerator which understands CUDA based software. For the industry, short-term available high power-consumption GPU cards are not the optimal solution. NVidia offers with the Jetson TX2 the combination of a multi-core ARM-based CPU and 256 GPU shader able to run your CUDA code.

But a vision application does not need just a CPU and GPU – it needs vision relevant interfaces. The VisionBox DAYTONA serves all the requirements: cameras can be connected with a single cable able to transport the data, power and especially the trigger signal. IO and an encoder interface support sensors and actors. And finally, for easy access from outside, a modem can be connected directly to your support team. 

All the functions are covered in an industrial, fanless, low power consumption housing.

The VisionBox DAYTONA - best to use for the following applications:

  • Deep Learning Inference Programs. While the learning phase of neural networks runs offline in the cloud or on a specialized computer, the executable program of deep learning, so-called inference programs runs on the VisionBox DAYTONA.

  • Hyperspectral Imaging: This is a new dimension of big data coming from cameras able to „see“ what the human doesn’t see. Up to 1 700 nm wavelength, SWIR based cameras create data to analyze e.g. material. The related algorithms need a CUDA based accelerator as it is available in the VisionBox DAYTONA.

  • Lightfield cameras: They are used in 3D applications and create many images from an object. To compute the amount of data, a GPU accelerator is welcome, too.

Your benefits

Suitable for deep learning and other applications requiring an GPU accelerator

Regarding the application development, first, you have a multi-core Linux computer on your desk. Under the Linux OS, you develop the application with the freedom to use CUDA operators running on the GPU.

Just to underline the differences: With the Linux OS, IMAGO provides VisionBoxes with x86 (i-Core) CPU inside, alternatively an 8-Core ARM Cortex A72 CPU. The VisionBox DAYTONA instead uses a Quad-Core ARM A57 together with a 256 shader GPU.

Connect your camera, sensors, PLC and ethernet interface. IO are managed by our real-time communication controller – covered in an SDK for Linux OS. Start to develop your application.

Supports vision relevant interfaces

GigE Power and Trigger over Ethernet: A single ethernet cable supporting all camera functions as power supply, image data transmission, setup of parameter and especially IMAGO’s trigger over ethernet which is supported by a number of camera makers.

Real-Time IO: Many applications do not need a hard real-time OS – but for peripherals, it is very important to receive input data, to create new output data via a logic and to serve the process timing as many machines are working very fast. IMAGO’s real-time communication controller is a key feature to guarantee your process timing.

Incremental Encoder Input: You need the speed of the conveyer belt? Not only for line scan applications but also together with area scan cameras, the information of the speed can become important. The Encoder Input is connected to the real-time communication controller with several logical functions to ensure a reliable application.

HDMI output: Connect directly a display to manage your software.

Compact, low power consumption, long-term availability

The VisionBox DAYTONA is only 163mm x 163mm x 48mm in size. The compact housing just consumes less than 25W. Computing power is combined with low power consumption. As with many IMAGO products, also the VisionBox DAYTONA is long-term available.




For example, in the food and beverage industry, more complex (deep learning) or big data applications (hyperspectral) become of interest. Integration into a stainless steel housing is easy thanks to the compact form factor and low-power consumption.


For example, hyperspectral analysis becomes important for pharmaceutical products. Once the applications run under the Linux OS, all validation processes become easier as the OS can be freezed for a long time. The VisionBox DAYTONA covers your application, nothing more.


In the production process, deep learning becomes more important. As an example, in the die casting industry, deep learning-based software is already used. To support self-sufficient applications, the HDMI output for the monitor is welcome.

Packaging & Logistics

For example, a computer installed on a forklift? Why not using the VisionBox DAYTONA, connect cameras and run more intelligent programs able to extract more data to control the forklift.

For example, with the GPU accelerator, e.g. code reading applications can be improved as in the logistics industry more and more codes are on packages.

For example, packaging machines themselves welcome the real-time behavior of the IO as they run very fast.

Target Group


Developers of machine vision applications using CUDA based software. Software engineers who are familiar with the Linux OS and Nvidia based processors.


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