🟠 YOLOv5 Segmentation

Function Description

The operator is based on deep learning models of YOLO architecture, performing instance segmentation on input images, quickly detecting targets in images and generating precise contours for each target.

Usage Scenarios

Suitable for scenarios requiring fast or real-time processing, or needing to obtain precise shape information of targets.

Input Output

Input

Image: Single color image to be segmented, must be in RGB format.

Output

Detection result: A detection instance list. Each element in the list represents an identified object, containing its class, confidence score and contour polygon.

Parameter Description

Weight File

Parameter Description

Upload pre-trained YOLO segmentation model.

Parameter Adjustment

Supports both .onnx and .epicnn formats:

  • .onnx: Suitable for running on regular hardware devices.

  • .epicnn: Suitable for running on smart camera devices.

Enable GPU

Parameter Description

When using .onnx model, sets whether to use CPU or GPU computation. Note: This option is invalid for .epicnn models.

Parameter Adjustment

  • Enabled (recommended): For .onnx models, enabling in environments with NVIDIA graphics cards can greatly improve processing speed.

  • Disabled (default): Uses CPU for computation, slower speed but no special hardware requirements.

Confidence Threshold

Parameter Description

The model gives each detected target a confidence score, only targets with scores exceeding this threshold will be considered valid results and output.

Parameter Adjustment

  • Increase threshold: More strict filtering, can effectively reduce false positives but may increase false negatives.

  • Decrease threshold: More lenient filtering, can find more targets but may introduce more false positives.

Parameter Range

[0,1], default value: 0.8