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Sidewalk semantic segmentation
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README.md

Sidewalk Semantic Segmentation

Build Status

logo

Live demo

here

About

This is the repository of our portfolio project in DSR. In this project, we built a classifier that can predict the surface category of sidewalks in Berlin from street view images. We developed this as a basis for the future routing application that can tell users road conditions.

Contributors

Models

The basis model is U-Net with VGG11 encoder pretrained with ImageNet.

We trained two models, one is a sidewalk detector, and the other is a surface category classifier. The two outputs were combined to make a final prediction.

Data

We collected 798 images from Google Street View and manually annotated them. We supplemented the dataset with 77 photos that we took ourselves. For the sidewalk detector, we further supplemented the dataset with 1424 images from Berkeley DeepDrive dataset.

We used 30 GSV images for the validation, and other 30 GSV images for the test.

Results

Binary classification test IoU

Category IoU
Sidewalk 0.838

Surface category classification test IoU

Category IoU
Flat Stones 0.547
Pavement Stone 0.462
Sett 0.602
Bicycle Tiles 0.419

Segmentation examples

example1 example2

Development

Install dependencies

pip3 install -r requirements.txt

Predict an image

Download trained weights

./download_weights.sh

Run a prediction

./predict.sh ./tests/resources/segmentation/labelme/JPEGImages/zyZ1BD8DoUJ2.jpg

This outputs segmented images to ./out

Train model

./train.sh

Run all tests

python -m pytest tests

Run interactive tests (This shows some images)

python -m pytest tests -m interactive --interactive

See training logs using tensorboard

tensorboard --logdir $LOGDIR

License

MIT

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