machine vision

Case Study – Machine Vision

Overview and Strategic Importance

The project addressed critical inefficiencies in construction project monitoring, traditionally reliant on manual, time-consuming, and error-prone processes. By leveraging computer vision technology, the platform enabled automated, real-time oversight, delivering precise, data-driven insights into project progress. This facilitated faster decision-making, optimized resource management, reduced financial and temporal risks, and enhanced safety compliance.

System Implementation

The solution was deployed in three phases:

  1. Visual Data Collection: Capturing real-time site data.
  2. 3D Reconstruction and BIM Comparison: Aligning site progress with planned models.
  3. Progress and Performance Analysis: Utilizing deep learning to evaluate project metrics.

A user-friendly managerial dashboard provided interactive insights, including progress percentages, productivity metrics, safety alerts, and discrepancies.

Performance Outcomes

  • Qualitative Impact:
    • Reduced project tracking and reporting time by 95%.
    • Enabled early issue detection, minimizing rework and ensuring compliance with safety regulations.
    • Transformed sites into “digital factories” with real-time data for predictive and preventive management.
    • Built a historical dataset for more accurate future project time and cost predictions.
  • Quantitative Impact:
    • Achieved over 95% accuracy in detecting structural components (IOU) and 90% in resource detection (MAP).
    • Reduced schedule variance by 15%.
    • Decreased rework costs by 20%.
    • Lowered recorded safety incident rates by 25%.
Improvement Trends

The platform significantly streamlined operations, reduced inefficiencies, and enhanced project oversight. The automation of monitoring processes minimized human error, while real-time analytics empowered proactive decision-making. The reduction in schedule deviations, rework costs, and safety incidents reflects substantial operational and safety improvements, positioning the project as a scalable model for future initiatives.You can find other implemented case studies here.

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