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Towards Robust Obje...
Towards Robust Object Detection in Floor Plan Images : A Data Augmentation Approach
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- Mishra, Shashank (författare)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany
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- Hashmi, Khurram Azeem (författare)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgarage, Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany
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- Pagani, Alain (författare)
- German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany
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- Liwicki, Marcus (författare)
- Luleå tekniska universitet,EISLAB
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- Stricker, Didier (författare)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany
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- Afzal, Muhammad Zeshan (författare)
- Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; Mindgarage, Department of Computer Science, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany; German Research Institute for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany
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(creator_code:org_t)
- 2021-11-25
- 2021
- Engelska.
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 11:23
- Relaterad länk:
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https://doi.org/10.3...
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https://www.mdpi.com...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- object detection
- Cascade Mask R-CNN
- floor plan images
- deep learning
- transfer learning
- dataset augmentation
- computer vision
- Maskininlärning
- Machine Learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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