# Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach
## 2 Related Work
In [31], the author attempted to parse floor plan images using deep learning detectors. The author used Cascade Mask R-CNN [15] to extract the information from a floor plan image and used keypoint-CNN in segmentation to find precise locations of corners, which is further combined in the post-processing step to give the resulting segmentation. In [32], the authors attempt to synthesize a textual description from a floor plan image. This is another good application for floor plan analysis. This can help visually impaired people to imagine the interiors of the house, and it is also helpful for potential buyers of the house who are located far. The authors detect walls by performing morphological closure on the input floor plan image; doors are detected using the scale-invariant feature, and then connected components are identified using the flood fill technique. Once this information was available, then text processing was applied. In another work presented by [PERSON] et al. [33], the authors proposed a method for floor plan recognition, with a focus on recognizing diverse floor plan elements, e.g., walls, doors, rooms, and furniture. The authors used a shared VGG [34] encoder to extract features from the input floor plan images. It detects the room-boundary for walls, windows, and doors. It also detects the room type based on the elements in the room. The number of furniture items used to identify the room type is less. However, the authors get good results in detecting walls, windows, and doors. In one of the recent works on floor plans, the authors [11] created a framework for floor plan recognition and reconstruction. The authors used text detection as well as symbol detection to identify room types. Symbol detection is identifying different furniture objects available in the room and, based on it, determining the type of the room. The authors use YOLO4 [35] as a base network for identifying symbols in different rooms. This is also supported by the information from the text detection. Once all the required information is present, vectorization is performed on the floor plan images to reconstruct a new floor plan image. In [10], the authors presented a method to detect the elements in the floor plan images, wall, and windows, as well as to determine the text from floor plan images. The authors used a fully convolutional network (FCN) and optical character recognition (OCR) technique. Experiments were performed on CVC-FP [7] and self-collected datasets. The experiments performed on the datasets were: wall segmentation, object detection, and text recognition. Although promising wall segmentation was reported, the number of testing samples to evaluate the object detection and text recognition performance was relatively low. Another work for object detection in floor plan images was done by [PERSON] et al. [9], where the authors analyzed different available datasets in the floor plan domain and created their own custom datasets. They used Faster R-CNN [12] with ResNet-101 [19] as backbone for detecting furniture objects in the floor plan images. The custom dataset used in this experiment has fewer furniture class objects and fewer samples. Thus, the work does not provide conclusive empirical evidence to verify the effectiveness of the proposed method. From the results, we can identify that the network, which was pre-trained on COCO [26] dataset, performs well. Based on all these works, we can identify that furniture object detection is the pre-liminary step in processing floor plan images irrespective of the application. Whether we want to generate some text-based synthesis for floor plan images or we want to reconstruct the floor plan images, we must identify the objects available in different rooms of the floor plan and identify doors and windows correctly. Our work mainly focuses on identifying the furniture objects, windows, and walls in floor plan images, creating a base for all the applications mentioned above.