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
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