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  11. Construction Jobsite Image Classification Using An Edge Computing Framework

Construction Jobsite Image Classification Using an Edge Computing Framework

Gongfan Chen1, Abdullah Alsharef2, Edward Jaselskis1

  • 1Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA.

Sensors (Basel, Switzerland)
|October 26, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an efficient edge computing framework for real-time image classification on construction sites. The system enables AI applications without internet, improving project monitoring and safety.

Area of Science:

  • Construction Technology
  • Artificial Intelligence
  • Edge Computing

Background:

  • Real-time image classification on construction sites is vital for project monitoring but limited by on-site computing resources and poor connectivity.
  • Existing solutions struggle with remote sites lacking telecommunication support or experiencing signal attenuation.

Purpose of the Study:

  • To propose an efficient edge-computing-enabled image classification framework for real-time construction AI applications.
  • To develop a system that overcomes the limitations of on-site computing and internet dependency.

Main Methods:

  • Developed a lightweight binary image classifier using MobileNet transfer learning and quantization.
  • Assembled a complete edge computing hardware module (Raspberry Pi, Edge TPU, battery).
Keywords:
Edge TPURaspberry Piconstruction image classificationedge computing

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  • Integrated a multimodal software module (visual, textual, audio data) for intelligent classification.

    • Main Results:

    • The framework successfully synchronized multimodal data for zero-latency classification.
    • Demonstrated effectiveness in material classification and safety detection (e.g., identifying hazardous nails) without internet connectivity.
    • The system maintained accuracy while reducing model size through quantization.

    Conclusions:

    • The proposed edge computing framework enables real-time AI applications on construction sites, even in remote locations.
    • Construction managers can utilize this system for centralized management, enhancing accuracy and safety without additional investment.
    • This research facilitates edge intelligence for future construction, promoting human-technology interaction without high-speed internet requirements.
    material classification
    quantization
    safety detection
    transfer learning