Technical Papers
Dec 30, 2022

Cost-Efficient Image Semantic Segmentation for Indoor Scene Understanding Using Weakly Supervised Learning and BIM

Publication: Journal of Computing in Civil Engineering
Volume 37, Issue 2

Abstract

Image segmentation is an essential step in vision sensing and image processing. It enables the understanding of the object’s classes, spatial locations, and extents in the scene, which can be used to support a wide range of construction applications such as progress monitoring, safety management, and productivity analysis. The recent ground-breaking achievements of deep learning-based approaches for semantic segmentation are at the cost of expensive large-scale training datasets annotated at the pixel level. Although building information modeling (BIM) has been leveraged to alleviate labeling costs using automatically generated, color-coded images as semantic labels, the differences between the BIM models and the real-world scenes make it difficult to apply networks trained on BIM-generated labels to real images. Furthermore, it takes nontrivial efforts to reduce such differences. To address these problems, this paper proposes a weakly supervised segmentation approach that uses inexpensive image-level labels. The missing boundary information in image-level labels is compensated by BIM-extracted object information. The proposed method consists of three modules: (1) detect initial object locations from image-level labels; (2) extract object information from BIM as prior knowledge; and (3) incorporate the prior knowledge into the network to enhance the detected object locations. Three extensive experiments are designed to evaluate the effectiveness of the proposed method. Results show that the proposed method substantially improves the detected object areas by using prior knowledge of target objects from BIM and outperforms the state-of-the-art weakly supervised methods.

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Data Availability Statement

The annotations for the image datasets and the proposed models are available from the corresponding author upon reasonable request.

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Journal of Computing in Civil Engineering
Volume 37Issue 2March 2023

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Received: Jun 26, 2022
Accepted: Nov 14, 2022
Published online: Dec 30, 2022
Published in print: Mar 1, 2023
Discussion open until: May 30, 2023

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Ph.D. Student, Division of Construction Engineering and Management, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907. Email: [email protected]
Professor, Division of Construction Engineering and Management, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907 (corresponding author). ORCID: https://orcid.org/0000-0003-4527-1974. Email: [email protected]

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ASCE Library Card (5 downloads)
$105.00
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ASCE Library Card (20 downloads)
$280.00
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Buy Single Article
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ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

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