Comparison of Directional and Diffused Lighting for Pixel-Level Segmentation of Concrete Cracks

Visual inspections of concrete infrastructure in low-light environments require external lighting to ensure adequate visibility. Directional lighting sources, where an image scene is illuminated with an angled lighting source from one direction, can enhance the visibility of surface defects in an image. This paper compares directional and diffused scene illumination images for pixel-level concrete crack segmentation. A novel directional lighting image segmentation algorithm is proposed, which applies crack segmentation image processing techniques to each directionally lit image before combining all images into a single output, highlighting the extremities of the defect. This method was benchmarked against two diffused lighting crack detection techniques across a dataset with crack widths typically ranging from 0.07 mm to 0.4 mm. When tested on cracked and uncracked data, the directional lighting method significantly outperformed other benchmarked diffused lighting methods, attaining a 10% higher true-positive rate (TPR), 12% higher intersection over union (IoU), and 10% higher F1 score with minimal impact on precision. Further testing on only cracked data revealed that directional lighting was superior across all crack widths in the dataset. This research shows that directional lighting can enhance pixel-level crack segmentation in infrastructure requiring external illumination, such as low-light indoor spaces (e.g., tunnels and containment structures) or night-time outdoor inspections (e.g., pavement and bridges).

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Publication Details

Journal
Infrastructures
Published
2025-05-25
DOI
https://doi.org/10.3390/infrastructures10060129
Primary Topic
Infrastructure Maintenance and Monitoring
Type
article
Field-Weighted Citation Impact
0.00

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article

Comparison of Directional and Diffused Lighting for Pixel-Level Segmentation of Concrete Cracks

Marcus Perry, Sanjeetha Pennada, Hamish Dow, Jack McAlorum
Infrastructures
Infrastructure Maintenance and Monitoring
article

Comparison of Directional and Diffused Lighting for Pixel-Level Segmentation of Concrete Cracks

Marcus Perry, Sanjeetha Pennada, Hamish Dow, Jack McAlorum
article en

Abstract

Visual inspections of concrete infrastructure in low-light environments require external lighting to ensure adequate visibility. Directional lighting sources, where an image scene is illuminated with an angled lighting source from one direction, can enhance the visibility of surface defects in an image. This paper compares directional and diffused scene illumination images for pixel-level concrete crack segmentation. A novel directional lighting image segmentation algorithm is proposed, which applies crack segmentation image processing techniques to each directionally lit image before combining all images into a single output, highlighting the extremities of the defect. This method was benchmarked against two diffused lighting crack detection techniques across a dataset with crack widths typically ranging from 0.07 mm to 0.4 mm. When tested on cracked and uncracked data, the directional lighting method significantly outperformed other benchmarked diffused lighting methods, attaining a 10% higher true-positive rate (TPR), 12% higher intersection over union (IoU), and 10% higher F1 score with minimal impact on precision. Further testing on only cracked data revealed that directional lighting was superior across all crack widths in the dataset. This research shows that directional lighting can enhance pixel-level crack segmentation in infrastructure requiring external illumination, such as low-light indoor spaces (e.g., tunnels and containment structures) or night-time outdoor inspections (e.g., pavement and bridges).

InfrastructuresVol. 10(6)
University of Strathclyde (GB)
Scottish Funding Council
Openalex Percentile: Top 88%
Infrastructure Maintenance and Monitoring
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Comparison of Directional and Diffused Lighting for Pixel-Level Segmentation of Concrete Cracks — Marcus Perry, Sanjeetha Pennada, et al. · Infrastructures (2025) | TGRS Research Map | TGRS