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AI-Box - Smart Visual Inspection

AI-Box is our edge computer to solve visual inspection tasks right on your production line. Learning from real-world inspection cases that we implemented over the last years, we developed hardware and software to address the needs of an AI-driven inspection workflow.

 

Inspect Images

The core of AI-Box runs a deep learning algorithm that has “learned” how to distinguish images from “OK” and “not OK” products. Using dedicated hardware, deep neural networks are executed to efficiently infer from any input image an output label. This output label represents the answer of the visual inspection and can be displayed or sent to another machine.

Unlike traditional rule-based inspection, our AI is trained on labeled images that you provide. This enables a new workflow where the bottleneck is no longer the vision engineer coding the inspection, but where the AI itself learns how to inspect.

GigE Vision and GeniCam

Supporting cameras through industry standards means you can choose from a wide range of options of grayscale, color, or even 3D cameras to suit your use case.

Power over Ethernet (PoE)

With PoE, cameras can directly connect data and power with a single cable, supporting up to 100m in length.

Real-time GPIO’s

A dedicated FPGA enables real-time control of GPIOs for optimal setup of triggers for light and camera as well as other external peripherals. 

Acquire Images

Any visual inspection starts with taking images through cameras. With AI-Box, cameras and lights can be directly connected without the need for intermediate switches or controllers. AI-Box configures the cameras and triggers light and camera to take images that allow for a detailed surface inspection. Using High-Dynamic-Range (HDR) photography, even highly reflective surfaces can be acquired with sufficient local contrast.

Binary and multi-class classification

for each input image, the AI algorithm returns a single OK/NOK answer. Or you extend the training data by including different error classes, and our algorithm learns to distinguish multiple types of defects

Image
Segmentation

for each input image, the AI algorithm can show exactly where the defect is in the image.

Anomaly
detection

in cases where the defect covers a large part of the image, our algorithm recognizes when an image contains an anomaly not seen before. This algorithm can be trained utilizing only images of good parts and does not require the customer to provide images of defects.

AI-based solutions require a dedicated workflow, this is supported by AI-Box: 

1.

Create a new product, add classes or defect types you want to identify, and start acquiring and annotating images. 

2.

Review images, remove wrong labels or adapt annotations.

3.

Let Deevio create a deep neural network-based model that is tailored to your new product and upload it to AI-Box

4.

Change your inspection to the new model and start inspecting on the production line!​

Manage Your Inspection Case

Create, review, adapt and export data sets that are ready to train an AI model.

​Annotate images with use case-specific labels. Draw on top of images with our labeling tool to mark the exact location of a defect.

Data export: No lock-in, you can easily export images and annotation data from AI-Box

Specs

The AI-Box application is optimized to run on the AI-Box, a small and efficient machine vision PC that is built to our design.

In addition, the software stack runs on a wide range of NVIDIA-GPU-enabled PCs to serve any requirements for cycle time, image resolution, and complexity of the AI-models. 

Low energy 

WIFI, 4G

2xPoE/1x Gigabit Ethernet

Digital IO

24V DC

General

System on a Chip (SoC)

Nvidia Tegra X2, 64 Bit

CPU 

4-core ARM Cortex-A57, 2GHz

2-core Denver2, 2GHz

GPU 

256 CUDA cores (Pascal), 1.12 GHz

RAM

8 GB DDR4

Storage

1 x M.2 SSD (256 GB)

1 x 32 GB eMMC

1 x SDXC slot

Interfaces

Camera Interface

2 x GigE Vision with Trigger-and-Power-over-Ethernet

Camera Trigger

2 x Trigger-Over-Ethernet

Digital I/O

8 x Input & 8 x Output, opto-isolated, 24 V

Encoder Interface RS-422

3 x input for one encoder

LAN

1 x 1000 MBit/s

Wireless LAN

1 x IEEE 802.11a/b/g/n/ac with external antenna socket

LTE (4G)

1 x internal socket for SIM, with external antenna socket

USB

2 x USB 3.0 ports, 1 x micro USB maintenance port 

Video

1 x DisplayPort

Power & Dimensions

Power Supply

24 V (DC) 

Thermal Solution

fan-less, via heat sink

Dimensions (W x D x H)

163 mm x 163 mm x 48 mm (without mounting plate)

163 mm x 210 mm x 48 mm (with mounting plate)

deevio GmbH

Swinemünder Straße 121

10435 Berlin

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deevio is supported by the Pro FIT Program of the Investitionsbank Berlin. The goal of the cooperation is the research and development of continuous learning algorithms for automated quality control. This project is co-financed by the European Fund for Regional Development (EFRE).