gahrb/deep-learning-for-solar-panel-recognition

CNN models for Solar Panel Detection and Segmentation in Aerial Images.

Deep-Learning-for-Solar-Panel-Recognition

Recognition of photovoltaic cells in aerial images with Convolutional Neural Networks (CNNs). Object detection with YOLOv5 models and image segmentation with Unet++, FPN, DLV3+ and PSPNet.

💽 Installation + pytorch CUDA 11.3

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With pip:

pip install -r requirements.txt && pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113

With Anaconda:

pip install -r requirements.txt && conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

💻 How to start?

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OBJECT DETECTION

1. Specify the location of the data in sp_dataset.yaml.
2. Preprocess and generate annotations with yolo_preprocess_data.py and create_yolo_annotations.py respectively.
3. Run yolo_train.py for training.
4. Run yolo_detect.py for inference.

SEGMENTATION

1. Specify the structure of the data in segmentation/datasets.py
2. The code to train and run segmentation models can be found in the notebooks section.

🔍 Data sources

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• ☀ Solar Panels Dataset

  Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery (https://zenodo.org/record/5171712)

• 🌍 Google Maps Aerial Images

  • GoogleMapsAPI: src/data/wrappers.GoogleMapsAPIDownloader
  • Web Scraping: src/data/wrappers.GoogleMapsWebDownloader

• 📡 Sentinel-2 Data (unused)

  Sentinel-2 Satellite data from Copernicus. src/data/wrappers.Sentinel2Downloader

🛠 Processing pipeline

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🧪 Models

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• Object Detection

  • YOLOv5-S: 7.2 M parameters
  • YOLOv5-M: 21.2 M parameters
  • YOLOv5-L: 46.5 M parameters
  • YOLOv5-X: 86.7 M parameters

  Architectures are based on YOLOv5 repository.

  Download all the models here.

• Image Segmentation

  • Unet++: ~20 M parameters
  • FPN: ~20 M parameters
  • DeepLabV3+: ~20 M parameters
  • PSPNet: ~20 M parameters

  Architectures are based on segmentation_models.pytorch repository.

  Download all the models here.

📈 Results

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• Metrics

• Dataset and Google Maps images

🌐 Project Organization

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── data               <- Data for the project (ommited)
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks.
│        ├── segmentation_pytorch_lightning.ipynb     <- Segmentation modeling with Pytorch Ligthning.
│        └── segmentation_pytorch.ipynb               <- Segmentation modeling with vanilla Pytorch.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│        ├── figures        <- Generated graphics and figures to be used in reporting
│        ├── Solar-Panels-Project-Report-UC3M         <- Main report
│        └── Solar-Panels-Presentation-UC3M.pdf       <- Presentation slides for the project.
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│       ├── __init__.py    <- Makes src a Python module
│       │
│       ├── data           <- Scripts to download or generate data
│       │       ├── download.py   <- Main scripts to download Google Maps and Sentinel-2 data. 
│       │       ├── wrappers.py   <- Wrappers for all Google Maps and Sentinel-2.
│       │       └── utils.py      <- Utility functions for coordinates operations.
│       │
│       ├── features       <- Scripts to turn raw data into features for modeling
│       │       ├── create_yolo_annotations.py   <- Experimental script to create YOLO annotations.
│       │       └── yolo_preprocess_data.py      <- Script to process YOLO annotations.
│       │
│       ├── models         <- Scripts to train models and then use trained models to make predictions
│       │       ├── segmentation  <- Image segmentation scripts to train Unet++, FPN, DLV3+ and PSPNet models.
│       │       └── yolo          <- Object detection scripts to train YOLO models.
│       │
│       └── visualization  <- Scripts to create exploratory and results oriented visualizations
│            └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

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