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

Only applicable to Faster-RCNN

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

Only applicable to Faster-RCNN

In which order the input dimensions are:

  • Width / Height

  • Height / Width

Output layer to use

Only applicable to YOLOv5

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.

If 0 no maximum area is defined.

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.

TIP In the analyze tree add the descriptor ”Segmentation label” and select a text file including the names of objects in the ONNX file.

Max objects

Max number of objects in image, takes the first