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Updated: Jan 16, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Directional Lighting-Based Deep Learning Models for Crack and Spalling Classification.

Sanjeetha Pennada¹, Jack McAlorum¹, Marcus Perry¹

¹Department of Civil & Environmental Engineering, University of Strathclyde, Glasgow G1 1XJ, UK.

Journal of Imaging

|September 26, 2025

Summary

This summary is machine-generated.

This study introduces directional lighting techniques for concrete crack detection, outperforming traditional methods. A multi-channel neural network achieved superior accuracy in classifying concrete defects under low-light conditions.

Keywords:

hyperparameter tuning image fusion multi-channel neural network stratified cross-validation structural health monitoring transfer learning

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Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

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Published on: January 6, 2023

Area of Science:

Civil Engineering
Computer Vision
Artificial Intelligence

Background:

Autonomous inspections of concrete structures require effective external lighting, especially in low-light conditions.
Uniformly diffused lighting has limitations in detecting complex crack patterns in concrete.
Existing methods struggle with accurate concrete defect classification under challenging illumination.

Purpose of the Study:

To propose novel algorithms using directional lighting for improved concrete defect classification.
To address the limitations of uniformly diffused lighting in crack detection.
To enhance the accuracy and reliability of autonomous concrete inspections.

Main Methods:

Developed two algorithms: a fused neural network and a multi-channel neural network.
Fused neural network utilizes maximum intensity pixel-level image fusion from directional images.
Multi-channel neural network creates a five-channel image representing different lighting directions (Right, Down, Left, Up, Diffused).

Main Results:

The multi-channel neural network model demonstrated superior performance.
Achieved high evaluation metrics: 96.6% accuracy, 96.3% precision, 97% recall, and 96.6% F1 score.
Outperformed the FusedNet and other existing literature models without increasing evaluation time.

Conclusions:

Directional lighting significantly improves concrete crack classification accuracy.
The multi-channel neural network is a promising approach for autonomous concrete inspections in low-light environments.
Future work will explore extending these techniques to white-box methods.