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Publication Type : Conference Proceedings
Thematic Areas : Center for Computational Engineering and Networking (CEN)
Source : 3rd International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021; Raipur; India; 11 December 2021
Campus : Coimbatore
School : Computational Engineering and Networking
Center : Computational Engineering and Networking
Verified : No
Year : 2023
Abstract : Building information modelling (BIM) software uses scalable vector formats to enable flexible designing of floor plans in the industry. Floor plans in the architectural domain can come from many sources that may or may not be in scalable vector format. The conversion of floor plan images to fully annotated vector images is a process that can now be realized by computer vision. Novel data sets in this field have been used to train convolutional neural network (CNN) architectures for object detection. Image enhancement through super-resolution (SR) is also an established CNN-based network in computer vision that is used for converting low-resolution images to high-resolution ones. This work focuses on creating a multi-component module that stacks a SR model on a floor plan object detection model. The proposed stacked model shows greater performance than the corresponding vanilla object detection model. For the best case, the inclusion of SR showed an improvement of 39.47% in object detection over the vanilla network.
Cite this Research Publication : Khare D., Kamal N.S., Barathi Ganesh H.B., Sowmya V., Sajith Variyar V.V. "Enhanced Object Detection in Floor Plan Through Super-Resolution", 3rd International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021; Raipur; India; 11 December 2021