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July 29, 2026Open Access

Automated Structural Deficiency Detection: An Enterprise-Scale Computer Vision Approach

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Authors

SBSrivathsan Logantha Bhaskaran

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Overview

Randomized trial demonstrates enhanced defect detection in infrastructure, suggesting improved safety and efficiency.

Key Points

  • This research aims to improve the detection of structural deficiencies in roofing using automated computer vision techniques.
  • Developed an enterprise-scale automated defect detection pipeline using a ConvNeXt-Base model.
  • Trained the model on a dataset of over 125,000 images across 42 deficiency classes.
  • Applied a dual-stage mitigation strategy for class imbalance through inverse frequency sampling and label-smoothed loss.
  • Achieved an overall weighted F1-score of 0.53 across a validation set of 25,500 samples.
  • Critical failure categories like structural punctures and debris accumulation had F1-scores exceeding 0.85.
  • Demonstrated commercial viability for advanced AI in large-scale infrastructure monitoring.

Cite This Study

Srivathsan Logantha Bhaskaran (2026) studied this question.

synapsesocial.com/papers/6a69a28ac8da07d9defa6172https://doi.org/10.5281/zenodo.21630675
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