Object Detection in Floor Plan Images

Abstract

In this work we investigate the use of deep neural networks for object detection in floor plan images. Object detection is important for understanding floor plans and is a preliminary step for their conversion into other representations. In particular, we evaluate the use of object detection architectures, originally designed and trained to recognize objects in images, for recognizing furniture objects as well as doors and windows in floor plans. Even if the problem is somehow easier than the original one in the case of this research the datasets available are extremely small and therefore the training of deep architectures can be problematic. In addition to the use of object detection architectures for floor plan images, another contribution of this paper is the creation of two datasets that have been used for performing the experiments covering different types of floor plans with different peculiarities.