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Skeleton-Based Activity Recognition for Process-Based Quality Control of Concealed Work via Spatial-Temporal Graph

Lei Xiao¹, Xincong Yang², Tian Peng³

¹Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China.

Sensors (Basel, Switzerland)

|February 24, 2024

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This summary is machine-generated.

This study introduces a computer vision framework using Spatial-Temporal Graph Convolutional Networks (ST-GCNs) for real-time construction quality control. The model accurately recognizes plastering activities and their order, enabling detection of missing or misplaced steps.

Area of Science:

Construction Engineering and Management
Computer Vision
Artificial Intelligence

Background:

Computer vision (CV) automates construction site monitoring but is underutilized for process-based quality control, particularly for concealed works.
Current methods lack real-time, automated analysis of construction sequences and quality adherence.
There is a need for advanced techniques to ensure quality in hidden construction processes.

Purpose of the Study:

To develop and validate a framework for process-based quality control in construction using Spatial-Temporal Graph Convolutional Networks (ST-GCNs).
To enable automated recognition of construction activities and their temporal order for quality assessment.
To address the gap in applying CV for real-time quality control of concealed construction works.

Main Methods:

Keywords:

ST-GCN activity recognition construction progress management quality control

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A framework utilizing Spatial-Temporal Graph Convolutional Networks (ST-GCNs) was developed.
An on-site plastering work video dataset was collected for experimental validation.
The ST-GCN model was trained to recognize four primary plastering activities and their sequence.

Main Results:

The ST-GCN model achieved 99.48% accuracy in recognizing plastering activities on the validation set.
The model successfully identified correct activity sequences, missing activities (e.g., fiberglass mesh covering), and incorrect activity orders in test videos.
Activity order recognition was effective, allowing for convenient judgment of process integrity.

Conclusions:

The developed ST-GCN framework offers a promising approach for active, real-time, process-based quality control in construction.
This technology can significantly enhance quality assurance for concealed works by automating sequence and activity verification.
The study demonstrates the potential of advanced CV techniques to improve construction process monitoring and defect detection.