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The important convolution properties include width, area, differentiation, and integration properties.
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Deep Learning for Detecting Building Defects Using Convolutional Neural Networks.

Husein Perez1, Joseph H M Tah2, Amir Mosavi2

  • 1Oxford Institute for Sustainable Development, School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK. hperez@brookes.ac.uk.

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Summary
This summary is machine-generated.

This study introduces a convolutional neural network (CNN) model for automated building defect detection. The AI accurately identifies and locates issues like mould and stains from images, improving building maintenance efficiency.

Keywords:
building defectsclass activation mapping (CAM)convolutional neural networks (CNN)deep learningstructural-health monitoringtransfer learning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Structural Health Monitoring

Background:

  • Traditional building condition assessments are time-consuming, costly, and pose safety risks.
  • Clients require faster, more frequent building surveys for proactive maintenance.
  • Existing methods involve manual inspections, delaying essential repairs.

Purpose of the Study:

  • To evaluate Convolutional Neural Networks (CNNs) for automated detection and localization of building defects from images.
  • To develop a robust and scalable solution for real-time building defect identification.
  • To compare the performance of VGG-16, ResNet-50, and Inception models for this task.

Main Methods:

  • Utilized pre-trained CNN classifiers (VGG-16, ResNet-50, Inception) for defect detection.
  • Employed Class Activation Mapping (CAM) for precise defect localization within images.
  • Identified and analyzed challenges and limitations for real-world application.

Main Results:

  • The proposed CNN model demonstrated robustness in accurately detecting and localizing key building defects such as mould, deterioration, and stains.
  • Comparative analysis showed the effectiveness of different CNN architectures.
  • The approach proved capable of identifying defects from visual data.

Conclusions:

  • CNNs offer a viable and effective solution for automated building defect detection and localization.
  • The developed model has the potential for real-time application using mobile devices and drones.
  • This technology can significantly enhance the efficiency and safety of building maintenance and repair.