• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Deep Window Detection in Street Scenes

Vol. 14, No. 2, February 29, 2020
10.3837/tiis.2020.02.022, Download Paper (Free):


Windows are key components of building facades. Detecting windows, crucial to 3D semantic reconstruction and scene parsing, is a challenging task in computer vision. Early methods try to solve window detection by using hand-crafted features and traditional classifiers. However, these methods are unable to handle the diversity of window instances in real scenes and suffer from heavy computational costs. Recently, convolutional neural networks based object detection algorithms attract much attention due to their good performances. Unfortunately, directly training them for challenging window detection cannot achieve satisfying results. In this paper, we propose an approach for window detection. It involves an improved Faster R-CNN architecture for window detection, featuring in a window region proposal network, an RoI feature fusion and a context enhancement module. Besides, a post optimization process is designed by the regular distribution of windows to refine detection results obtained by the improved deep architecture. Furthermore, we present a newly collected dataset which is the largest one for window detection in real street scenes to date. Experimental results on both existing datasets and the new dataset show that the proposed method has outstanding performance.


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Cite this article

[IEEE Style]
W. Ma and W. Ma, "Deep Window Detection in Street Scenes," KSII Transactions on Internet and Information Systems, vol. 14, no. 2, pp. 855-870, 2020. DOI: 10.3837/tiis.2020.02.022.

[ACM Style]
Wenguang Ma and Wei Ma. 2020. Deep Window Detection in Street Scenes. KSII Transactions on Internet and Information Systems, 14, 2, (2020), 855-870. DOI: 10.3837/tiis.2020.02.022.

[BibTeX Style]
@article{tiis:23281, title="Deep Window Detection in Street Scenes", author="Wenguang Ma and Wei Ma and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.02.022}, volume={14}, number={2}, year="2020", month={February}, pages={855-870}}