Use the model

You can use this model in the Detect Objects Using Deep Learning tool available in the Image Analyst toolbox in ArcGIS Pro. Follow the steps below to use the model for detecting seabirds in images.

Detect seabirds

Complete the following steps to detect seabirds from the imagery:

1. Download the Seabird (Tern) Detection—Africa model and add the imagery layer in ArcGIS Pro.
2. Zoom to an area of interest.

   

3. Browse to Tools on the Analysis tab.

   

4. Click the Toolboxes tab in the Geoprocessing pane, select Image Analyst Tools, and browse to the Detect Objects Using Deep Learning tool under Deep Learning.

   

5. Set the variables on the Parameters tab as follows:
   1. Input Raster—Select the imagery.
   2. Output Detected Objects—Set the output feature class that will contain the detected objects.
   3. Model Definition—Select the pretrained or fine-tuned model .dlpk file.
   4. Model Arguments (optional)—Change the values of the arguments if required.
      • padding—Number of pixels at the border of image tiles from which predictions are blended for adjacent tiles. Increase its value to smooth the output while reducing edge artifacts. The maximum value of the padding can be half of the tile size value.
      • batch_size—Number of image tiles processed in each step of the model inference. This depends on the memory of your graphics card.
      • threshold—The detections with a confidence score higher than this threshold are included in the result. The allowed value ranges from 0 to 1.0.
      • return_bboxes—If set to True, the tool will return a bounding box around the detected feature.
      • tile_size—The width and height of image tiles into which the imagery is split for prediction.
   5. Non Maximum Suppression—Optionally, check the check box to remove the overlapping features with lower confidence.

      If checked, do the following:

      • Set Confidence Score Field.
      • Set Class Value Field (optional).
      • Set Max Overlap Ratio (optional).

        

6. Set the variables on the Environments tab as follows:
   1. Processing Extent—Select Current Display Extent or any other option from the drop-down menu.
   2. Cell Size (required)—Set the value as resolution of the imagery. You can keep the default value to pick the imagery extent by default.
   3. Processor Type—Select CPU or GPU.

      It is recommended that you select GPU, if available, and set GPU ID to the GPU to be used.

      

7. Click Run.

   The output layer is added to the map.

   

8. Click the Toolboxes tab in the Geoprocessing pane, select Data Management Tools, and browse to the Feature Envelope To Polygon tool under Features.

   

9. Set the variables on the Parameters tab as follows:
   1. Input Features—Select the output from the detected object using deep learning.
   2. Output Feature Class (required)—Set the output feature class that will contain the detected objects within the bounding box.

   

10. Click Run.

    The output layer is added to the map.