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Updated: Sep 11, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Structural Component Identification and Damage Localization of Civil Infrastructure Using Semantic Segmentation.

Piotr Tauzowski¹, Mariusz Ostrowski¹, Dominik Bogucki^1,2

¹Institute of Fundamental Technological Research, Polish Academy of Sciences, 02-106 Warsaw, Poland.

Sensors (Basel, Switzerland)

|August 14, 2025

Summary

Automating civil infrastructure inspection using deep learning shows promise. Convolutional neural networks trained on synthetic data achieved high accuracy in identifying structural components and damage, demonstrating potential for real-world applications.

Keywords:

computer vision-based techniques semantic segmentation structural health monitoring

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Area of Science:

Civil Engineering
Computer Vision
Artificial Intelligence

Background:

Visual inspection of civil infrastructure is costly and time-consuming.
Automating structural health assessment is a significant research area.
Deep neural networks offer potential for automated inspections.

Purpose of the Study:

To train and evaluate U-net and DeepLab v3+ convolutional neural networks for structural health assessment.
To develop a synthetic dataset for training and validation.
To assess the performance of deep learning models on real-world infrastructure images.

Main Methods:

Training U-net and DeepLab v3+ on a synthetic Tokaido dataset.
Utilizing semantic segmentation masks for component and damage identification.
Applying data augmentation techniques to enhance the synthetic dataset.

Main Results:

Models achieved 97% accuracy and 87% Mean Intersection over Union (mIoU) on validation data.
Promising results in semantic segmentation of real-world structures.
DeepLab v3+ outperformed U-net in identifying structural components, but not damage.

Conclusions:

Deep learning models trained on synthetic data can effectively perform structural health assessment.
The synthetic Tokaido dataset is a valuable resource for training inspection models.
DeepLab v3+ shows superior performance for component identification in infrastructure assessment.