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Easily Configurable Visual Inspection System for Industrial Applications
Andrla, Ondřej ; Špaňhel, Jakub (referee) ; Španěl, Michal (advisor)
The aim of this thesis is to create a prototype of an easily configurable camera system, used to control products on an automated assembly line. The thesis is especially focused on the issue of LED detection. Their very presence in the image is detected and the system also evaluates whether the LED is lit and, if so, in what color. The final solution allows the user to easily define control metric for searching for circular objects in the image in a configuration application with a graphical user interface. The configuration is saved in a JSON file. Furthermore, a runtime which independently processes the recording from the camera, checks the products and reports on them to other components of the system based on the configuration was implemented. The system uses the Hough circular transformation to detect objects in the image. To test the efficiency of the system, a dataset was created using the FitKit tool with a LED matrix. When testing the runtime on the dataset, the overall accuracy of LED detection was 97,79%.
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Easily Configurable Visual Inspection System for Industrial Applications
Andrla, Ondřej ; Špaňhel, Jakub (referee) ; Španěl, Michal (advisor)
The aim of this thesis is to create a prototype of an easily configurable camera system, used to control products on an automated assembly line. The thesis is especially focused on the issue of LED detection. Their very presence in the image is detected and the system also evaluates whether the LED is lit and, if so, in what color. The final solution allows the user to easily define control metric for searching for circular objects in the image in a configuration application with a graphical user interface. The configuration is saved in a JSON file. Furthermore, a runtime which independently processes the recording from the camera, checks the products and reports on them to other components of the system based on the configuration was implemented. The system uses the Hough circular transformation to detect objects in the image. To test the efficiency of the system, a dataset was created using the FitKit tool with a LED matrix. When testing the runtime on the dataset, the overall accuracy of LED detection was 97,79%.
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