Object-Bin Segmentation
Function Description
Based on the Object-Bin Segmentation model package, this step segments workpieces and bins from input depth and color images, outputs workpiece and bin masks, and provides visualization results.
Usage Scenario
This step is suitable for scenarios where workpieces and bins need to be effectively separated. It is generally preceded by camera-capture steps and followed by point-cloud extraction steps.
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Go to Download Center to obtain the Object-Bin Segmentation deep-learning model package. |
Input and Output
Input
| Input Port | Data Type | Description |
|---|---|---|
Camera Depth Image |
Image/Depth |
Original depth image of objects. |
Camera Color Image |
Image/Color |
Original color image of objects. |
Output
| Output Port | Data Type | Description |
|---|---|---|
Visualization Output |
Image/Color |
Visualization result. |
Workpiece Present |
Bool |
Workpiece detection result of input image. |
Workpiece Mask Image |
Image/Color/Mask |
Workpiece mask image obtained by segmentation. |
Bin Mask Image |
Image/Color/Mask |
Bin mask image obtained by segmentation. |
System Requirements
To use this step, the following system requirements must be met.
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CPU: Must support AVX2 instruction set, and meet either of the following conditions:
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Without a discrete GPU: Intel i5-12400 or above.
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With a discrete GPU: Intel i7-6700 or above, and GPU no lower than GeForce GTX 1660.
The feature has been fully tested on Intel CPUs and has not yet been tested on AMD CPUs. Intel CPUs are recommended.
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GPU: Use GeForce GTX 1660 or above (if a discrete GPU is installed).
Model Package Settings
- Model Manager Tool
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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” or “.dlkpackC” file exported from Mech-DLK.
Tuning instruction: Please refer to Deep Learning Model Package Management Tool for the usage.
- Model Name
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Parameter description: This parameter is used to select the model package that has been imported for this Step.
Tuning instruction: Once you have imported the deep learning model package, you can select the corresponding model name in the drop-down list.
- DI Algo Type Translated String
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Parameter description: Once a Model Name is selected, the DI Algo Type Translated String will be filled automatically.
- GPU ID
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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
- ROI Path
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Parameter description: This parameter is used to set or modify the ROI.
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.
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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. |
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If you would like to use the default ROI again, please delete the ROI file name below the Open the editor button. |
Post-Processing
| Parameter | Description |
|---|---|
Morphological Transformation |
Description: When enabled, morphological processing is applied to segmentation results of workpieces and bins. Default value: Disabled. |
Morphological Transformation Type |
Description: Used to select morphological post-processing method for masks. Value list: Dilation, Erosion
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Kernel Size |
Description: Used to set kernel size of morphological transformation. Larger kernels produce stronger effects. Default value: 3 px Adjustment recommendation: Adjust kernel size according to actual requirements. |
