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Vision Starter Project

Short Description

This guided project introduces the basic workflow of the Vision Starter Kit.
You will configure the first image acquisition settings, create a simple AI Classification task and set up an Anomaly Detection example.

Project type Required knowledge level Estimated duration Additional hardware and software requirements
Guided Project Basic 90 Minutes none – everything included in the Starter Kit

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Goal

The goal of this project is to get familiar with the basic workflow of the Vision Starter Kit and SICK Nova.

After completing this project, you should be able to:

  • acquire images with the InspectorP61x
  • adjust basic image acquisition settings
  • create a simple AI Classification task
  • use the Object Locator
  • configure a basic AI Anomaly Detection task
  • reset the application to default settings

Instructions

Follow the steps below to complete the Vision Starter Project.

1. Image acquisition

  • Set up the Inspector as described in Getting started.
  • Select Live on top and press the Play button at the bottom to take images continuously.
  • Place the GitHub info card in the camera's field of view
  • Select Jobs > Acquisition and play around with the Settings to get a well-lit image. Alternatively, press Run auto setup directly.

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  • If necessary, adjust the focus of the camera manually using the focus adjustment tool (included in the Kit)

2. AI Classification

  • Choose Analysis > Add tool > Classify > AI Classification (without dStudio)

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  • Adjust the size of the box, so that it encompasses the entire object plus a buffer to account for variations or different positions.

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  • Create classes under Dataset on the right
  • Click the play icon at the bottom center to take continuous photos
  • Expand the first class and capture images using Add active image
  • Try out different variations (position / rotation)

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  • Repeat for the second class, take at least 5 images each and click on Train.

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  • After a few seconds, the training is finished an you can test the results.

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  • If useful, add more images or include additional classes (e.g., "Empty") to optimize the results.

3. Object Locator

The Object Locator is designed to detect the position of an object and perform analyses relative to that position (e.g., detect anomalies).

  • On the left, choose Jobs > Analysis > Add tool > Locate > Object Locator
  • Place an object in the image
  • At the bottom center, click Update reference and jump to the Reference tab
  • Drag the box over a distinctive part of the object

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  • If necessary, adjust the parameters on the right:
  • Edge strength for contrast edges
  • Rotation for possible rotation of the object
  • Scaling, Match score and Angle for sensitivity
  • Click Live and Play at the center to test whether the part of the object is being tracked when the object is moved

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Important

The Object Locator must work reliably before additional tools are used.
Add additional tools directly below the Object Locator.

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4. Anomaly Detection

  • On the left, choose Jobs > Analysis > Add tool > Verify > AI Anomaly Detection (if necessary, below Object Locator)

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  • At the top center, select the Reference tab and drag a box over the object (make it only slightly larger, since the Object Locator is tracked along with it)

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  • Go back to Live and Play and find Dataset at the bottom right. Add Good images with various variations. Start with just a few Good images for now.
  • Click Train.

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Adjust training parameters if necessary:

  • Number of training images (the rest is for evaluation)
  • Fast vs. precise depending on the number of images and time
  • Keep Strict matching active if object variation is very low / only one object

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  • Set to Live and Play and test out different positions and foreign objects.
  • At the bottom right under Results, adjust Anomaly Score and Visualization range if necessary. This affects the sensitivity of detecting anomalies and visualizing them with a heatmap.

Dependency on Object Locator

Anomaly Detection depends on the Object Locator.
If the Object Locator fails, Anomaly Detection will also fail.

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If necessary, capture additional images and also add Bad images to optimize results

5. Reset

At the op right, click on the 3 dots > Application defaults

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Expected Result

After completing this project, the Vision Starter Kit should be configured for:

  • image acquisition
  • simple AI Classification
  • object localization
  • basic AI Anomaly Detection

You should now understand the basic workflow of SICK Nova and how different analysis tools can be combined.

Summary

In this guided project, you learned how to acquire images, classify objects, locate objects and detect anomalies with the Vision Starter Kit.

This project is intended as the first practical demo after completing the Getting Started guide.

Next Steps

Continue with another Vision example project or open the complete project files on GitHub.com.

Example Projects

GitHub