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Image-Enhanced U-Net: Optimizing Defect Detection in Window Frames for Construction Quality Inspection

Author(s): ORCID


Medium: journal article
Language(s): English
Published in: Buildings, , n. 1, v. 14
Page(s): 3
DOI: 10.3390/buildings14010003
Abstract:

Ensuring the structural integrity of window frames and detecting subtle defects, such as dents and scratches, is crucial for maintaining product quality. Traditional machine vision systems face challenges in defect identification, especially with reflective materials and varied environments. Modern machine and deep learning (DL) systems hold promise for post-installation inspections but face limitations due to data scarcity and environmental variability. Our study introduces an innovative approach to enhance DL-based defect detection, even with limited data. We present a comprehensive window frame defect detection framework incorporating optimized image enhancement, data augmentation, and a core U-Net model. We constructed five datasets using cell phones and the Spot Robot for autonomous inspection, evaluating our approach across various scenarios and lighting conditions in real-world window frame inspections. Our results demonstrate significant performance improvements over the standard U-Net model, with a notable 7.43% increase in the F1 score and 15.1% in IoU. Our approach enhances defect detection capabilities, even in challenging real-world conditions. To enhance the generalizability of this study, it would be advantageous to apply its methodology across a broader range of diverse construction sites.

Copyright: © 2023 by the authors; licensee MDPI, Basel, Switzerland.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10753911
  • Published on:
    14/01/2024
  • Last updated on:
    07/02/2024
 
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