# Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach
###### Abstract  
Object detection is one of the most critical tasks in the field of Computer vision. This task comprises identifying and localizing an object in the image. Architectural floor plans represent the layout of buildings and apartments. The floor plans consist of walls, windows, stairs, and other furniture objects. While recognizing floor plan objects is straightforward for humans, automatically processing floor plans and recognizing objects is challenging. In this work, we investigate the performance of the recently introduced Cascade Mask R-CNN network to solve object detection in floor plan images. Furthermore, we experimentally establish that deformable convolution works better than conventional convolutions in the proposed framework. Prior datasets for object detection in floor plan images are either publicly unavailable or contain few samples. We introduce SFPI, a novel synthetic floor plan dataset consisting of 10,000 images to address this issue. Our proposed method conveniently exceeds the previous state-of-the-art results on the SESYD dataset with an mAP of 98.1%. Moreover, it sets impressive baseline results on our novel SFPI dataset with an mAP of 99.8%. We believe that introducing the modern dataset enables the researcher to enhance the research in this domain. object detection; Cascade Mask R-CNN; floor plan images; deep learning; transfer learning; dataset augmentation; computer vision +
Footnote †: journal: Medical Imaging  
0000-0001-6000-0001-6000][PERSON] 1,0000-0001-6000-0001-6000-0001-6000-0001-6000-0001-6000-00  
various sofa objects are present. Similarly, additional rooms are identified with different furniture objects. This type of floor plan is useful for interactive furniture fitting [5]. In Figure 2, the top left room contains a coffee table and a sink, whereas the bottom left room contains a chair and sofa. Similarly, the remaining rooms have other furniture objects. Contrarily to Figure 1, apart from the localization of furniture object, no other information is available in Figure 2. Identifying the furniture objects present in a floor plan image enables us to distinguish the type of room, i.e., kitchen, living room, and so on. We also have a few common elements like furniture objects, walls, windows, and doors. These are some common artifacts that exist in every floor plan image [3]. Identifying the floor plan objects is the preliminary step towards the analysis of the floor plan images. Our main work focuses on this preliminary step to detect the furniture objects, doors, and windows in the floor plan images. Detecting these objects is difficult due to the variety of floor plan layouts available. Furthermore, individual furniture objects are not always similar, i.e., we can have multiple types of chairs and windows in a floor plan image. One of the critical ideas of our research is to create an end-to-end trainable framework that can handle different varieties of furniture objects and generalize well. In order to develop a robust learning-based model, we need a large-scale dataset that should include different varieties of floor plan layouts and furniture objects. There exist only a few publicly available datasets for floor plan images [4; 7; 8]. Besides fewer samples, less variation in floor plan layouts and furniture objects are also a concern for the floor plan datasets. It is challenging to train deep detectors using the currently available floor plan datasets. We have created our custom dataset to fill this gap, containing 10,000 images with various floor plans and furniture objects. This dataset will be available publicly for further enhancement and experiments. Figure 3 represents a floor plan where all furniture objects are masked and highlighted based on the bounding box information and corresponding class labels that are present in the ground truth of the SESYD dataset [3]. Figure 1: Sample floor plan image with the specification of different room sizes and furniture objects. Each variety of floor plan layout has different floor plan elements, shapes, and sizes.