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.
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.
After a few seconds, the results are available:
stats
output layers
predicted mask
predicted mask with Bing map background