Defect Segmentation

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Function

Use the Defect Segmentation model package to perform inference on the input image. This model can locate and segment the defect regions in the image and output the defect class information. Single-class or multi-class defect segmentation model packages trained with Mech-DLK can be imported.

This model is suitable for detecting surface defects such as stains, bubbles, and scratches in industries such as renewable energy, electronics, PCB, printing, and daily necessities manufacturing.

intro

Input and Output

After importing the model package in the Deep Learning Model Package Inference Step, the following input and output ports will be displayed.

Input

Input port Data type Description

Image

Image/Color

Image input to this port will be used for deep learning model package inference.

Output

When the imported model package is a single-class defect segmentation model package, the output ports are as follows:

Output port Data type Description

Visualization Output

Image/Color

Visualized results.

Segmentation Boolean Result

Bool

A Boolean result that indicates the presence of a defect. True indicates that the input image is free of object defects. False indicates that the input image contains object defects.

Segmentation Mask

Image/Color/Mask

Non-zero pixel values indicate detected defects.

Segmentation Mask Contours

Shape2D/Contour[]

List of pixel coordinates of the vertices of the segmentation result mask.

Defect Classes

String

Defect types recognized in images.

When the imported model package is a multi-class defect segmentation model package, the output ports are as follows:

Output port Data type Description

Defect Segmentation/Label 1

DLResult/DefectSegmentation

Label the defect segmentation results of class 1.

Defect Segmentation/Label 2

DLResult/DefectSegmentation

Label the defect segmentation results of class 2.

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When importing a multi-class defect segmentation model package in this Step, connect each output port to the Deep Learning Result Parser Step to output the defect segmentation results for each labeled class separately.

Parameter Description

The following parameters need to be adjusted when the defect segmentation model package is imported into this Step.

Model Package Settings

Parameter Description

Model Manager Tool

Parameter description: This parameter is used to open the deep learning model package management tool and import the deep learning model package. The model package file is a “.dlkpack” file exported by Mech-DLK.
Tuning instruction: Please refer to Deep Learning Model Package Management Tool for the usage.

Model Name

Parameter description: This parameter is used to select the model package that has been imported for this Step.
Tuning instruction: After importing a deep learning model package with the Deep Learning Model Package Management Tool, select the corresponding model package name from the drop-down list.

Release Original Model Package After Switching

Parameter description: This parameter determines whether to release the resources occupied by the original model package immediately when the model package is switched.
Default setting: Selected.
Instruction: Once this option is selected, when the Step switches to another model package, the system will immediately release the original model package resources, even if the model package is still in use by other Steps. When this option is not selected, the system will automatically release the original model package only when it is no longer used by any Steps.

Model Package Type

Parameter description: Once a Model Name is selected, the DI Algo Type Translated String will be filled automatically.

Input Batch Size

Parmeter description: The number of images processed during each inference.

GPU ID

Parameter description: This parameter is used to select the device ID of the GPU that will be used for the inference.
Tuning instruction: Once you have selected the model name, you can select the GPU ID in the drop-down list of this parameter.

Pre-Process

Parameter Description

ROI File

Parameter description: This parameter is used to set or modify the ROI of the input image.

Tuning instruction: Once the deep learning model is imported, a default ROI will be applied. If you need to edit the ROI, click Open the editor. Edit the ROI in the pop-up Set ROI window, and fill in the ROI name.

Before the inference, please check whether the ROI set here is consistent with the one set in Mech-DLK. If not, the recognition result may be affected.

During the inference, the ROI set during model training, i.e. the default ROI, is usually used. If the position of the object changes in the camera’s field of view, please adjust the ROI.

If you would like to use the default ROI again, please delete the ROI file name below the Open the editor button.

Post-Process

Parameter Description

Inference Configuration

Parameter description: This parameter is used to configure parameters related to defect segmentation model package inference. You can click Open the editor to open the inference configuration window.
Instruction: Please refer to Inference Configuration Tool for detailed parameter description.

Visualization Settings

Parameter Description

Draw result on image

Parameter description: Once enabled, the detection result will be displayed on the image.
Default value: Disabled.
Tuning recommendation: Please set this parameter according to your actual needs.

Customize Font Size

Parameter description: This parameter determines whether to customize the font size in the visualized outputs. Once this option is selected, you should set the Font Size (0–10).
Default value: Disabled.
Tuning recommendation: Please set this parameter according to your actual needs.

Font Size (0–10)

Parameter description: This parameter is used to set the font size in the visualized outputs.
Default value: 1.5
Tuning recommendation: Please set this parameter according to your actual needs.

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