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Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach

Applied sciences, 2021-12, Vol.11 (23), p.11174 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2076-3417 ;EISSN: 2076-3417 ;DOI: 10.3390/app112311174

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  • Title:
    Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach
  • Author: Mishra, Shashank ; Hashmi, Khurram Azeem ; Pagani, Alain ; Liwicki, Marcus ; Stricker, Didier ; Afzal, Muhammad Zeshan
  • Subjects: Algorithms ; Architecture ; Cascade Mask R-CNN ; Computer vision ; Data augmentation ; dataset augmentation ; Datasets ; Deep learning ; Experiments ; floor plan images ; Floorplans ; Floors ; Furniture ; Localization ; Machine Learning ; Maskininlärning ; object detection ; Semantics ; Sensors ; transfer learning
  • Is Part Of: Applied sciences, 2021-12, Vol.11 (23), p.11174
  • Description: 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2076-3417
    EISSN: 2076-3417
    DOI: 10.3390/app112311174
  • Source: AUTh Library subscriptions: ProQuest Central
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