From Event: SPIE Smart Structures + Nondestructive Evaluation, 2023
Existing automated concrete inspection methods are intractable: capturing images under ambient conditions which can vary substantially. Furthermore, an opportunity may have been overlooked: utilizing illumination techniques to enhance defect contrast during imaging which may improve automatic defect detection accuracy. In this work, we present a robotic-mountable lighting apparatus that implements contrast enhancing illumination techniques in an automated package in order to improve crack detection and classification in concrete. Geometrical lighting techniques; directional and angled, were tested on three cracked concrete slab samples. Results from blind/reference less image spatial quality evaluation (BRISQUE) show that both directional and varied angled lighting influence the quality in different associated regions in an image. Furthermore, the region-based crack detection algorithm Faster R-CNN attained a higher accuracy when images were enhanced with directional lighting during all samples tested. The direction of highest accuracy was not consistent over samples, and is likely dependant on features such as crack location, width, orientation etc. This emphasises the importance of adaptive lighting: illuminating the surface with the most suitable conditions based on an initial observation of the feature or defect. This system represents the initial step in a fully automated and optimised concrete inspection system capable of defect capture, classification, localization and segmentation.
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Jack McAlorum, Marcus Perry, Hamish Dow, and Sanjeetha Pennada, "Robotic concrete inspection with illumination-enhancement," Proc. SPIE 12486, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023, 124860L (Presented at SPIE Smart Structures + Nondestructive Evaluation: March 14, 2023; Published: 18 April 2023); https://doi.org/10.1117/12.2655938.