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Related Experiment Video

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Bottleneck Detection in Modular Construction Factories Using Computer Vision.

Roshan Panahi¹, Joseph Louis¹, Ankur Podder²

¹School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, USA.

Sensors (Basel, Switzerland)

|April 28, 2023

Summary

This study introduces a computer vision method for modular construction factories to monitor production progress and identify bottlenecks. The new approach improves accuracy and adaptability, preventing project delays.

Keywords:

bottleneck detection computer vision deep learning modular construction sensors

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Area of Science:

Construction Management
Industrial Engineering
Computer Vision

Background:

Modular construction offers safety, quality, and productivity benefits but faces challenges with manually intensive processes causing variable cycle times and production bottlenecks.
Existing computer vision methods for monitoring modular construction progress struggle with adaptability, annotation effort, and changes in unit appearance.

Purpose of the Study:

To develop an adaptable computer vision-based progress monitoring system for modular construction factories.
To address the limitations of current methods by reducing annotation requirements and improving robustness to visual changes.

Main Methods:

Utilized Scale-Invariant Feature Transform (SIFT) for modular unit identification at workstations.
Employed Mask R-CNN deep learning for active workstation identification.
Integrated workstation status with a data-driven bottleneck identification method for assembly lines.

Main Results:

Achieved 96% accuracy in workstation occupancy identification and an 89% F-1 Score for station state identification.
Successfully detected bottleneck stations using extracted active and inactive workstation durations.
Validated the framework using 420 hours of surveillance video from a U.S. modular construction factory.

Conclusions:

The proposed computer vision method offers an adaptable and efficient solution for monitoring modular construction production lines.
Timely bottleneck identification through this system can prevent production delays and enhance overall productivity.
Implementation facilitates continuous monitoring, leading to improved efficiency in modular construction factories.