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.