YOLOv5 Oil Storage object detection

The following example shows how to use the YOLOv5 model for object detection on satellite images.

Dataset

The example is based on the Airbus Oil Storage Detection dataset. It provides satellite images with 100 cm/px resolution. Annotation bounding boxes for the planes are provided.

YOLOv7

We built our pipeline based on the YOLOv5 repository and using the yolov5m config.

Converting to onnx

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

python3 export.py --weights best.pt --include onnx --imgsz 512 --simplify

Example inference

Run QGIS, next add Bing map using QuickMapServices plugin.

../_images/example_oil_input_image.webp

Then run our plugin and set parameters like in the screenshot below. You can find the pre-trained onnx model at examples/yolov5_oils_detection_bing_map/model_yolov5_m_oils_512_1c.onnx path. Push the Run button to start processing.

../_images/example_oil_params.webp

After a few seconds, the results are available:

  • stats

    ../_images/example_oil_stats.webp

  • output layers

    ../_images/example_oil_layers.webp

  • predicted mask

    ../_images/example_oil_output_mask.webp

  • predicted mask with Bing map background

    ../_images/example_oil_output_map.webp