# Window Detection In Facade Imagery: A Deep Learning Approach Using Mask R-CNN

arXiv: https://arxiv.org/abs/2107.10006 · HTML: https://mlsanity.com/paper/2107.10006/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification](https://mlsanity.com/paper/1602.01517/) (2016)
- [Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey](https://mlsanity.com/paper/2001.04074/) (2020)
- [Fast and Regularized Reconstruction of Building Faccades from Street-View Images using Binary Integer Programming](https://mlsanity.com/paper/2002.08549/) (2020)
- [Visual Estimation of Building Condition with Patch-level ConvNets](https://mlsanity.com/paper/1804.10113/) (2018)

## Abstract (arXiv)

The parsing of windows in building facades is a long-desired but challenging task in computer vision. It is crucial to urban analysis, semantic reconstruction, lifecycle analysis, digital twins, and scene parsing amongst other building-related tasks that require high-quality semantic data. This article investigates the usage of the mask R-CNN framework to be used for window detection of facade imagery input. We utilize transfer learning to train our proposed method on COCO weights with our own collected dataset of street view images of facades to produce instance segmentations of our new window class. Experimental results show that our suggested approach with a relatively small dataset trains the network only with transfer learning and augmentation achieves results on par with prior state-of-the-art window detection approaches, even without post-optimization techniques.