Instance Segmentation

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Function

Use the instance segmentation model package to run inference on the input image. The model package segments the contour of each target object and outputs class labels.

This model is suitable for scenarios requiring accurate recognition and positioning of individual objects, such as depalletizing and palletizing, target object loading and unloading, and goods picking.

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.

Input port

Data type

Description

Image

Image/Color

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

Output

Output port	Data type	Description
Visualization Output	Image/Color	Visualized results.
Instance Masks	Image/Color/Mask[]	Masks of target objects detected. Non-zero pixel values are masks. The mask contour is the contour of the target object.
Instance Bounding Boxes	Shape2D/Contour[]	Detected bounding box of target object.
Instance Bounding Box Masks	Image/Color/Mask[]	Mask of the object bounding box. Non-zero pixel values are masks.
Instance Confidences	Number[]	Confidence of detected objects.
Instance Labels	String[]	Object labels.

Output port

Data type

Description

Visualization Output

Image/Color

Visualized results.

Instance Masks

Image/Color/Mask[]

Masks of target objects detected. Non-zero pixel values are masks. The mask contour is the contour of the target object.

Instance Bounding Boxes

Shape2D/Contour[]

Detected bounding box of target object.

Instance Bounding Box Masks

Image/Color/Mask[]

Mask of the object bounding box. Non-zero pixel values are masks.

Instance Confidences

Number[]

Confidence of detected objects.

Instance Labels

String[]

Object labels.

Parameter Description

The following parameters need to be adjusted when the instance 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.

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 instance 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.

If you use Mech-Vision 2.2.0 to open an instance segmentation project in Mech-Vision 2.1.2 or below that has the "dilation" parameter configured, please reset the Morphological Transformation parameters in the inference configuration tool. Otherwise, the original parameters cannot take effect.

Class Display Mode

Parameter description: Select to display classes in the output results by name or by index.

Visualization Settings

Parameter	Description
Draw Instance 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.
Visualization Method for Results	Parameter description: This parameter is used to specify the instance color scheme in the visualized output result. Value list: Display each instance, Display instances by class, Display instance center point Default setting: Display each instance Tuning recommendation: Please set this parameter according to your actual needs. Please refer to the tuning examples for the corresponding result.
Customize Font Size	Parameter description: This parameter determines whether to customize the font scale in the visualized output result. 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 scale in the visualized output result. Default value: 1.5 Tuning recommendation: Please set this parameter according to your actual needs.

Parameter

Description

Draw Instance 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.

Visualization Method for Results

Parameter description: This parameter is used to specify the instance color scheme in the visualized output result.
Value list: Display each instance, Display instances by class, Display instance center point
Default setting: Display each instance
Tuning recommendation: Please set this parameter according to your actual needs. Please refer to the tuning examples for the corresponding result.

Customize Font Size

Parameter description: This parameter determines whether to customize the font scale in the visualized output result. 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 scale in the visualized output result.
Default value: 1.5
Tuning recommendation: Please set this parameter according to your actual needs.

Tuning Examples

Visualization Method for Results

Visualization Method for Results	Description	Image
Display each instance	Visualize each instance in a unique color.
Display instances by class	Visualize the displayed instances by class, and the objects of the same class have the same color.
Display instance center point	Visualize the center point of an instance. The instance color is related to the “Confidence Threshold”.

Description

Image

Display each instance

Visualize each instance in a unique color.

instances sample

Display instances by class

Visualize the displayed instances by class, and the objects of the same class have the same color.

classes sample

Display instance center point

Visualize the center point of an instance. The instance color is related to the “Confidence Threshold”.

central point sample

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