Deep Learning image segmentation

Object detection using pre-trained algorithms via ONNX. The object detection algorithms supported can be found under the parameter model type.

See ONNX image segmentation for more information.

Parameters

Model type

• Faster R-CNN

• YOLO v4

• YOLO v5

Onnx file

Select the pre-trained ONNX file for the selected model type.

Source

On which image the ONNX segmentation should be applied. The pseudo-rgb image or a painted prediction image.

Confidence

The confidence level required by the model for an object to be categorized.

Normalize the pixel values

If Normalize and center is used the values will be scaled to 0-1 before the is subtracted and the used in the division. See below.

• No normalization

• Normalize and center

Image dimension order

In which order the input dimensions are:

• Width / Height

• Height / Width

Output layer to use

Which type of output layer to use:

• Sigmoid layer

• Detection layer

Min area

The minimum number of pixels for an object to be included.

Max area

The maximum number of pixels for an object to be included.

Object filter

Use an expression to further exclude unwanted objects based on shape.

Properties that can be used for the Expression:

• Area

• Length

• Width

• Circumference

• Regularity

• Roundness

• Angle

• D1

• D2

• X

• Y

• MaxBorderDistance

• BoundingBoxArea

Shrink

Takes away x numbers of pixels at the borders of the objects included in images.

Separate

• Normal

  • Can have both separated and combined objects.

• Separate adjacent objects

  • All objects are defined separately.

• Merge all objects into one

  • All objects are defined as one.

• Merge all objects per row

  • All objects per row segmentation are defined as one.

• Merge all objects per column

  • All objects per column segmentation are defined as one.

Max objects

Max number of objects in image, takes the first