# OPTIMISING IMAGE CLASSIFICATION - Implementation of Convolutional Neural Network Algorithms to Distinguish Between Plans and Sections within the Architectural, Engineering and Construction (AEC) Industry (2019)
URL: https://www.associativetrails.com/writing/2022/04/30/optimising-image-classification-implementation-of-cnn-to-distinguish-between-plans-and-sections-2019/
Published: 2022-04-30 · Practice · Sheet 03
Summary: Notes on a 2019 paper by Alessandra Fabbri, M. Hank Haeusler, and Yannis Zavoleas

The paper Fabbri, Haeusler & Zavoleas · 2019

ResearchGate (https://www.researchgate.net/publication/333104914_OPTIMISING_IMAGE_CLASSIFICATION_Implementation_of_Convolutional_Neural_Network_Algorithms_to_Distinguish_Between_Plans_and_Sections_within_the_Architectural_Engineering_and_Construction_AEC_Industry) PDF 1.5 MB (https://www.associativetrails.com/assets/uploads/caadria2019_126.pdf)

- 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) (https://arxiv.org/abs/1507.06228), residual networks (He et al. 2016) (https://arxiv.org/abs/1512.03385), dense networks (Huang et al. 2017) (https://arxiv.org/abs/1608.06993)
- Deploy as executable or web application.