Skip to content

Computer vision for construction documentation

License

Notifications You must be signed in to change notification settings

ninhpham96/AECVision

Repository files navigation

AECVision

AECVision is an object detection project for the construction industry. The main goal is to deliver good models for various problems like:

  • Detection elements on plans to allow automatized tasks
  • Speed up documentation analysis
  • Create tools for future new computer vision projects

Information

Now project is base on YOLOv5 repo but in the future I will try other types of model architectures.

  • Input image resolution 1280x1280
  • Image file .jpg
  • For tagging i use Label Studio

Project pipeline

insert pipeline image

Avaible models

Name Classes Model architecture Number of training images [original/augmented]
traine_best 12 Classes (see train_results/traine_best/labels.jpg) YOLOv5m6 252 / 1204

master MIT License

How it works?

The model was trained on clear plans and with annotation but remember that the best results you get without many symbols on construction plans.

Detection

images

Screen detection

dddd past gif

Project roadmap

  • Collect and tag more images (up to 500)
  • Try to use Grayscale than RGB
  • Change number of classes (maybe only 3? Wall, Window, Door)
  • Evolve hyperparameters to get the best set (in YOLO)
  • Use other model architecture (than YOLO)
  • Think about switch from .jpg file to .png (avoid converting data lost)
  • Think about how to add more "contexts" to the model (like: you are in the kitchen if there are stove and there probably will be table)

Summary & Problems

First training

Contributing

Contributions are always welcome! I look for help in tagging and improving models. Feel free to improve this project.

Thanks

Thanks, YOLOv5 and Label Studio for your project and tutorials. Special thanks to my brother Marcin who help me with tagging.

About

Computer vision for construction documentation

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 100.0%