# Window Detection In Facade Imagery: A Deep Learning Approach Using Mask R-CNN Source: [Window Detection In Facade Imagery: A Deep Learning Approach Using Mask R-CNN]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:23:37.152Z Authors: Nils Nordmark, Mola Ayenew Published: 2021-07-21T11:00:01.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()