🟠 Instance Segmentation V1

Function Description

The operator can perform instance segmentation on input color images through deep learning. Upload a pre-trained deep learning model (.pth file), and use this model to analyze input images, finally outputting position, class, confidence score and other data for each identified object.

Usage Scenarios

In complex backgrounds or scenarios where objects are close to each other or overlapping, identify precise contours of objects, separate each object instance for independent analysis, and plan more reliable and fitting grasping poses.

Input Output

Input

Image: Single color image to perform instance segmentation.

Output

Detection result: A list containing all successfully identified object instances in the image. Each element in the list represents an object, containing the object’s class, confidence score, bounding rectangle box and contour polygon information.

Parameter Description

Only one image can be processed at a time. If multiple images are input, only the first image will be processed by default.

Model File

Parameter Description

Used to load trained deep learning models for instance segmentation.

Parameter Adjustment

Select and upload model files trained for specific application scenarios and target objects. The model quality directly determines the accuracy and robustness of results.

Parameter Range

File format required is .pth format.

Confidence Threshold

Parameter Description

Used to set the "reliability" threshold for model output results. When the model identifies each object, it will give a score between 0 and 1, indicating how reliable the identification result is. Only objects with scores higher than this threshold will be output as valid results.

Parameter Adjustment

  • Increase value: Filtering conditions become stricter. The operator will only output results that the model is very confident about. This will reduce false identifications, but may miss some real targets with unclear identification features or partial occlusion.

  • Decrease value: Filtering conditions become more lenient. The operator will output more possible targets, but may introduce more false identifications. If many non-target objects are being incorrectly output, you can try increasing this threshold; if obvious targets are not being identified, you can appropriately decrease this threshold.

Parameter Range

[0,1], default value: 0.9

Enable GPU Acceleration

Parameter Description

Controls whether the operator uses CPU or GPU for computation. Since deep learning models have large computational requirements, using GPU can greatly improve processing speed.

Parameter Adjustment

  • Disabled (default): The operator will use CPU for computation. Although it has good compatibility and doesn’t require special hardware, processing one image may take longer time, not suitable for scenarios with high speed requirements.

  • Enabled (recommended): If the local device supports GPU graphics card, it’s recommended to enable this option, which will greatly improve processing speed.