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Here is a list of detectors already trained. They can be executed in ContextCapture, Orbit Feature Extraction Pro and Reality Data Analysis Service to run Annotation jobs.Each detector was trained:
Meaning, while running on your dataset, each detector type can only be used for the same specific type of job.
The quality of the detection will depend on the similarity between your dataset and the training dataset’s description.
If using ContextCapture, we recommend you to update your version to the latest one.In case no detector fits your purpose, you are welcome to submit a help ticket from your personal portal describing your expectations.
Name
Detector Type
Description
Illustration
Links
Cracks Ortho
Orthophoto Segmentation
Detect cracks in concrete infrastructure to enable defect inspection workflows.Dataset used: drone + handheldResolution: around 1cm/pixGeographic area: multiple
RoofsA
Dataset used: vertical/aerial mapping cameraResolution: around 30cm/pixGeographic area: multiple
RoofsB
Dataset used: vertical/aerial mapping cameraResolution: around 7.5cm/pixGeographic area: Christchurch - New Zealand
Face & License plates
Photo Object Detection
Detect faces and license plates to enable anonymization workflows.Dataset Used: mobile mapping device - PanoramasResolution: N/AGeographic area: Western Europe
Cracks
Photo Segmentation
Here is a list of sample datasets. They can be used to test the detectors above and the use of services like RDAS. To use one of the examples, you must replace the absolute path inside the "References" tag of the example's ContextScene.xml file with the absolute path leading to where the images were saved.
Link
Orthophoto Segmentation / Roofs
Download
Photo Object / Face and License Plates
Photo Segmentation / Cracks