YOLOv7 planes object detection

The following example shows how to use the YOLOv7 model for object detection on satellite images.


The example is based on the Airbus Aircraft Detection dataset. It provides satellite images with 50 cm/px resolution. Annotation bounding boxes for the planes are provided.


We built our pipeline based on the YOLOv7 repository and using the cfg/training/yolov7-tiny.yaml config.

Converting to onnx

When model training is completed, export the model using the command below:

python export.py --weights yolov7-tiny.pt --grid --simplify --img-size 256 256

Example inference

Run QGIS, next add Google Eart map using QuickMapServices plugin.


Then run our plugin and set parameters like in the screenshot below. You can find the pre-trained onnx model at examples/yolov7_planes_detection_google_earth/model_yolov7_tiny_planes_256_1c.onnx path. Push the Run button to start processing.


After a few seconds, the results are available:

  • stats

  • output layers

  • predicted mask

  • predicted mask with Google Earth background