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Deep Learning on Construction Sites: A Case Study of Sparse Data Learning Techniques for Rebar Segmentation.

Suzanna Cuypers¹, Maarten Bassier¹, Maarten Vergauwen¹

¹Department of Civil Engineering, Geomatics Section, KU Leuven-Faculty of Engineering Technology, 9000 Ghent, Belgium.

Sensors (Basel, Switzerland)

|August 28, 2021

Summary

Weakly and semi-supervised deep learning models can automate construction site monitoring by reducing the need for pixel-level labeled data. These methods rival fully supervised approaches for tasks like rebar cover detection.

Keywords:

construction site monitoring cross-consistency training image segmentation remote sensing semi-supervised learning weakly-supervised learning

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Area of Science:

Computer Vision
Artificial Intelligence
Construction Engineering

Background:

Deep learning models automate construction site monitoring using remote sensing.
Pixel-level labeled data is a major drawback, requiring manual effort from skilled personnel.
Reducing training data needs is crucial for efficient automation.

Purpose of the Study:

Evaluate weakly and semi-supervised semantic segmentation models for construction site imagery.
Reduce the dependency on large, manually labeled datasets.
Compare different deep learning approaches for automated monitoring tasks.

Main Methods:

Compared fully, weakly, and semi-supervised semantic segmentation models.
Utilized recent models: IRNet, DeepLabv3+, and cross-consistency training.
Focused on detecting rebar covers for quality control as a case study.

Main Results:

Weakly and semi-supervised models achieved performance comparable to fully supervised models.
The majority of target objects (rebar covers) were successfully segmented.
Demonstrated feasibility of deep learning with minimal manual input.

Conclusions:

Weakly and semi-supervised learning offer efficient alternatives for construction site monitoring.
Stakeholders can leverage these methods to balance efforts and costs.
Provides insights into selecting appropriate deep learning training strategies.