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:
- For a specific purpose
- On a specific dataset
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. | ||
|
RoofsA |
Orthophoto Segmentation |
Dataset used: vertical/aerial mapping camera | ||
|
RoofsB |
Orthophoto Segmentation |
Dataset used: vertical/aerial mapping camera | ||
|
Face & License plates |
Photo Object |
Detect faces and license plates to enable anonymization workflows. | ||
|
Cracks |
Photo Segmentation |
Detect cracks in concrete infrastructure to enable defect inspection workflows. |
Here is a list of sample datasets. They can be used to test the detectors above and the use of services like RDAS.
|
Name |
Illustration |
Link |
|
Image Object / Face and License Plates |  | |
|
Image Segmentation / Cracks |  | |
|
Orthophoto Segmentation / Roofs |  |