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ScrubberWatch

The idea behind ScrubberWatch is to detect the level of emissions of incoming ships in the Hamburg Harbour using Computer Vision. Essentially, we try to classify whether the Scrubber is turned on based on the smoke the ships are emitting. For detailed information on the use-case, please refer to the One-Pager and the small technical report.

ScrubberWatch is the winning project of the EIT Digital DeepHack Hamburg. We thank all the mentors, the organizers and especially Carsten Bullemer for the valuable discussions, ideas and feedback.

Docs and Links

Scrubber Watch - Dashboard

The Dashboard is implemented in dash. To execute it, install the environment specified in the dashboard/requirements.txt via pip install -r requirements.txt. Then run python app.py within the dashboard directory.

dashboard demo

Beware: the code for the dashboard is hackathon-quality!

Analyzing Scrubber usage by detecting smoke on ships

  • all relevant files are in subfolder smoke_detection

Installation

  1. Download the dataset from kaggle and unpack it into smoke_detection. You should now have the following folder structure:

    ---smoke_detection/train/images/*.png
  2. unzip the additional smoke data ship_smokes_fume_pollution.zip and link it to the train folder

    cd smoke_detection
    unzip ship_smokes_fume_pollution.zip
    mv ship_smokes_fume_pollution train/images
  3. copy train_smoke.csv to train folder

    cp train_smoke.csv train
  4. setup environment

    conda create --name scrubberwatch python=3.7
    pip install -r requirements.txt
  5. train the recognition model

    python smoke_classification.py
  6. run inference on input image

    python predict_smoke.py image.jpg
  7. evaluation on validation set

    python smoke_validation.py

Results

Ship Labelling Tool

  • The ship labelling tool is a simple jupyter notebook with ipywidgets. It can be launched with
cd smoke_detection
jupyter-lab
# open ship_labeling_tool.ipynb

annotation tool demo

Authors

  • Maximillian Franz
  • Sandro Braun
  • Leander Kurscheidt

Acknowledgments

  • Carsten Bullemer - Thank you for valuable advice towards this project idea.

About

ScrubberWatch Project for EIT Digital DeepHack Hamburg implemented within less than 18 hours.

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