Abstract
In a highly digitalised world, this paper aims at closing the gap towards automatic digitisation from 2D architectural drawings. We present the new image dataset Plan, and Elevation Representations of Doors And Windows (Perdaw) which provides a baseline for different classification problems with varying complexity. We investigate the performance of three machine learning models in distinguishing different types of doors and windows in their plan and elevation views. Our findings show that Inception V3 slightly outperforms MobileNet V2, which suggests that the latter solves the same classification tasks with less computational resources with only a minimal compromise in accuracy. Among the three investigated models, ResNet50 yields the lowest quality metrics within a small margin. Overall, all models perform better at classifying building components in their elevation views compared to their plan views. We consistently observed that the models yield the best results with 100{\%} accuracy for the binary classification problems, and dropped to close to 70{\%} accuracy for the 40-class classification problems.
Original language | English |
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Title of host publication | Engineering Applications of Neural Networks |
Editors | Lazaros Iliadis , Ilias Maglogiannis , Antonios Papaleonidas , Elias Pimenidis , Chrisina Jayne |
Number of pages | 12 |
Publisher | Springer Nature Switzerland |
Publication date | Jun 2024 |
Pages | 288-300 |
ISBN (Print) | 978-3-031-62494-0 |
ISBN (Electronic) | 978-3-031-62495-7 |
DOIs | |
Publication status | Published - Jun 2024 |
Event | International Conference on Engineering Applications of Neural Networks - Corfu, Greece Duration: 27 Jun 2024 → 30 Jun 2024 https://eannconf.org/2024/ |
Conference
Conference | International Conference on Engineering Applications of Neural Networks |
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Country/Territory | Greece |
City | Corfu |
Period | 27/06/2024 → 30/06/2024 |
Internet address |
Series | Communications in Computer and Information Science |
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Volume | 2141 |
ISSN | 1865-0929 |
Keywords
- Architecture
- Convolutional Neural Networks
- Machine learning
- Floor plans
- Deep Learning
- Object Classification
- Technical Symbols
- Architectural Symbols