Concrete crack pixel-level segmentation: a comparison of scene illumination angle of incidence

Hamish Dow*, Marcus Perry, Jack McAlorum, Sanjeetha Pennada

*Corresponding author for this work

Research output: Contribution to journalConference Contributionpeer-review

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Abstract

Previous research has demonstrated how angled and directional lighting can enhance the detection of concrete cracks in low-light environments and outperform diffused lighting alternatives. This paper investigates the effect of different angles of incidence of directional lighting for concrete crack pixel-level segmentation. Five directional lighting datasets of cracked concrete slabs were captured, each using an angle of incidence of 10, 20, 30, 40, and 50 degrees, respectively. A directional lighting crack segmentation algorithm was applied to each lighting angle dataset. Algorithm output comparisons with ground truths revealed that the directional lighting method performed best on the 50-degree lighting dataset, obtaining a recall, precision, and F1 score of 68%, 81%, and 74%, respectively. However, qualitative analysis of the segmentation outputs on a sub-image scale revealed that towards the edges of the images, the segmentation performance of 30-degree lighting was significantly better, with results closely matching those of the ground truth. This research highlights that the lighting angle of incidence can increase the performance of directional lighting concrete crack segmentation depending on defect position. The results from this work have the potential to improve low-light environment concrete crack detection and monitoring.
Original languageEnglish
Number of pages8
Journale-Journal of Nondestructive Testing
VolumeEWSHM 2024
DOIs
Publication statusPublished - 1 Jul 2024
Event11th European Workshop on Structural Health Monitoring - Potsdam, Germany
Duration: 10 Jun 202413 Jun 2024
https://ewshm2024.com/

Keywords

  • image processing
  • defect detection
  • directional lighting
  • automated inspections
  • binary classification

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