Classification of Building Information Model (BIM) Structures with Deep Learning
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2018
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| _version_ | 1866912091034615808 |
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| author | Lomio, Francesco Farinha, Ricardo Laasonen, Mauri Huttunen, Heikki |
| author_facet | Lomio, Francesco Farinha, Ricardo Laasonen, Mauri Huttunen, Heikki |
| contents | In this work we study an application of machine learning to the construction industry and we use classical and modern machine learning methods to categorize images of building designs into three classes: Apartment building, Industrial building or Other. No real images are used, but only images extracted from Building Information Model (BIM) software, as these are used by the construction industry to store building designs. For this task, we compared four different methods: the first is based on classical machine learning, where Histogram of Oriented Gradients (HOG) was used for feature extraction and a Support Vector Machine (SVM) for classification; the other three methods are based on deep learning, covering common pre-trained networks as well as ones designed from scratch. To validate the accuracy of the models, a database of 240 images was used. The accuracy achieved is 57% for the HOG + SVM model, and above 89% for the neural networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1808_00601 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Classification of Building Information Model (BIM) Structures with Deep Learning Lomio, Francesco Farinha, Ricardo Laasonen, Mauri Huttunen, Heikki Computer Vision and Pattern Recognition Machine Learning In this work we study an application of machine learning to the construction industry and we use classical and modern machine learning methods to categorize images of building designs into three classes: Apartment building, Industrial building or Other. No real images are used, but only images extracted from Building Information Model (BIM) software, as these are used by the construction industry to store building designs. For this task, we compared four different methods: the first is based on classical machine learning, where Histogram of Oriented Gradients (HOG) was used for feature extraction and a Support Vector Machine (SVM) for classification; the other three methods are based on deep learning, covering common pre-trained networks as well as ones designed from scratch. To validate the accuracy of the models, a database of 240 images was used. The accuracy achieved is 57% for the HOG + SVM model, and above 89% for the neural networks. |
| title | Classification of Building Information Model (BIM) Structures with Deep Learning |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/1808.00601 |