Associative Trails

OPTIMISING IMAGE CLASSIFICATION - Implementation of Convolutional Neural Network Algorithms to Distinguish Between Plans and Sections within the Architectural, Engineering and Construction (AEC) Industry (2019)

Notes on a 2019 paper by Alessandra Fabbri, M. Hank Haeusler, and Yannis Zavoleas

An attempt to build a convolutional neural network to classify 1000 images into 3 categories. Accuracy peaked at 82%, no hyperparameter tuning was performed.

Read the paper: ResearchGate, PDF.

 machine-learning  bim  cad  construction

  • Build a CNN to classify drawings into sections, plans and elevations
  • 1,000 images used - 800 for training, 200 for model validation
  • Objective to achieve accuracy of 90% or more.
  • Gives lots of detail about the different iterations of models. First peaked at 52% accuracy over 128 epochs. Second peaked at 78% after increasing the number of convolutional layers. Third lowered the learning rate and peaked at 82%.
  • They recommend further adjustment of hyperparameters to improve the results, with more trainman images. Also consider looking at alternative CNN architectures - highway networks (Srivastava et al. 2015), residual networks (He et al. 2016), dense networks (Huang et al. 2017)
  • Deploy as executable or web application.
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