Automatic target detection - AutoGCP algorithm

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AutoGCPs algorithm automatically locates targets in images and detects their centers with pixel-level accuracy. It reduces the time needed for tie points marking. 

Supported use cases

Currently we support:

  • Three kinds of targets: square, diagonal, and Aeropoint.
  • Automatic detection is currently supported only on RGB images.
  • Both nadir and oblique flights.
     
    Note: The algorithm is able to detect targets on slightly oblique projects (required: 0° - 45°; recommended: 10° - 35°). The GSD of targets on images needs to be close to the average GSD of the project (recommended: 20% difference maximum, required: 50% difference maximum). We do not recommend tilting the camera too much or placing targets outside the mission.

Pix4D_AutoGCP_target_haar.jpg

Square

Pix4D_AutoGCP_target_diagonal.jpg

Diagonal

Pix4D_AutoGCP_target_aeropoint.jpg

Aeropoint

Best practices

AutoGCP algorithm is robust to variations in scale and rotation, and can work on different types of ground control points. Optimal results are obtained following these guidelines:

  • The targets on the ground should be placed sufficiently distant from each other. The spacing should be at least larger than the camera georeferencing horizontal uncertainty.
  • Images should not be blurry, and the targets should be clean.
  • The target should not be obstructed by any object above it nor by shadows.
     
    Example: Some examples below show potentially problematic targets for detection.

    Pix4D_AutoGCP_obstructed_1.jpg

    Text on target

    Pix4D_AutoGCP_obstructed_3.jpg

    Objects on target

    Pix4D_AutoGCP_obstructed_2.jpg

    Target below guard rails

  • If obstruction cannot be avoided, more targets than necessary can be used, to increase the chances that a sufficient number of targets is available for re-calibration or re-optimization even if some are not detected.
  • The target must be large enough in order to be detected, depending on the GSD.
     
    Example: As a rule of thumb, the diagonal of the target should at least be 20 pixels, but this number may vary depending on the image quality (e.g., blur, exposure, contrast).  For example, if the average GSD of the projects is 2.5 cm (1 inch), the recommended diagonal of the target is 50 cm (20 inch).
  • Good image overlap makes the AutoGCPs results more reliable, with each target visible in multiple images.
  • Only the CP targets in use should be visible. If unused targets are visible in the images, AutoGCPs can be misled.

AutoGCPs in Pix4D products

AutoGCP algorithm is implemented in:

  • PIX4Dcloud Advanced, an online platform for drone mapping, progress tracking, and site documentation.
  • PIX4Dengine, customizable photogrammetry reconstruction engine.
  • PIX4Dmatic, photogrammetry software for corridor and large scale mapping.
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