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Deep Object Detection for Complex Architectural Floor Plans with Efficient Receptive Fields

Published: 19 June 2023 Publication History

Abstract

Architectural floor plans play an important role in sharing the building information among engineers, designers, and clients. Automatic floor plan analysis can help in improving work efficiency and accuracy. Object detection and recognition are critical in understanding and analyzing a floor plan document. However, few research works have been conducted to date for automatic object detection in architectural floor plans. In this paper, a convolutional neural network, namely ArchNet, is proposed to detect various visual objects, such as door, window, and stairs. The ArchNet is a modified version of YOLO network, and consists of five modules: backbone, multiscale receptive fields, neck, head, and non-maximal suppression. In this paper, ArchNet is used to detect 13 object classes commonly found in architectural floor plans. Experimental results show that the proposed architecture can achieve a mean average precision of 75% which is superior compared to the state-of-the-art techniques.

References

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        CVIPPR '23: Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition
        April 2023
        93 pages
        ISBN:9798400700033
        DOI:10.1145/3596286
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Publication History

        Published: 19 June 2023

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        Author Tags

        1. Architectural floor plan
        2. Convolutional neural network
        3. Object detection
        4. Receptive fields

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        CVIPPR '23 Paper Acceptance Rate 14 of 38 submissions, 37%;
        Overall Acceptance Rate 14 of 38 submissions, 37%

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