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

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

Skeleton-based noise removal algorithm for binary concrete crack image segmentation

Image processing methods for automated concrete crack detection are often challenged by binary noise. Noise removal methods decrease the false positive pixels of crack detection results, often at the cost of a reduction in true positives. This paper proposes a novel method for binary noise removal and segmentation of noisy concrete crack images. The method applies an area threshold before reducing the pixel groups in the image to a skeleton. Each skeleton is connected to its nearest neighbour before the remaining short skeletons in the image are removed using a length threshold. A morphological reconstruction follows to remove all elements in the original noisy image that do not intersect with the skeleton. Finally, pixel groups in close proximity to the endpoints of the pixel groups in the resulting image are reinstated. Testing was conducted on a dataset of noisy binary crack images; the proposed method (Skele-Marker) obtained recall, precision, and F1 score results of 77%, 91%, and 84%, respectively. Skele-marker was compared to other methods found in literature and was found to outperform other methods in terms of precision and F1 score. The proposed method is used to make crack detection results more reliable, supporting the ever-growing demand for automated inspections of concrete structures

Robotic concrete inspection with illumination-enhancement

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/referenceless 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

Automated concrete crack inspection with directional lighting platform

This letter presents the development and performance evaluation of a novel platform for visual concrete crack inspection. Concrete surfaces are imaged using directional lighting to support accurate crack detection, classification, and segmentation. In addition to developing lab- and field-deployable hardware iterations, we outline customized convolutional neural networks and filters that leverage the directionally lit dataset. Crack classification and segmentation accuracies were both 10% higher than accuracies for standard imaging techniques with diffuse lighting, and crack widths of 0.1 mm were reliably detected and segmented. The major innovation described here is the combination of new hardware platforms for directional lighting, with a suite of algorithms that utilize the directionally lit dataset to improve crack detection and evaluation. This letter demonstrates that directional lighting can improve the performance and robustness of automated concrete inspection. This could be key in supporting the efforts of asset managers as they seek to automate inspections of their ageing populations of concrete assets

Threshold-based BRISQUE-assisted deep learning for enhancing crack detection in concrete structures

Automated visual inspection has made significant advancements in the detection of cracks on the surfaces of concrete structures. However, low-quality images significantly affect the classification performance of convolutional neural networks (CNNs). Therefore, it is essential to evaluate the suitability of image datasets used in deep learning models, like Visual Geometry Group 16 (VGG16), for accurate crack detection. This study explores the sensitivity of the BRISQUE method to different types of image degradations, such as Gaussian noise and Gaussian blur. By evaluating the performance of the VGG16 model on these degraded datasets with varying levels of noise and blur, a correlation is established between image degradation and BRISQUE scores. The results demonstrate that images with lower BRISQUE scores achieve higher accuracy, F1 score, and Matthew’s correlation coefficient (MCC) in crack classification. The study proposes the implementation of a BRISQUE score threshold (BT) to optimise training and testing times, leading to reduced computational costs. These findings have significant implications for enhancing accuracy and reliability in automated visual inspection systems for crack detection and structural health monitoring (SHM)

Performance evaluation of an improved deep CNN-based concrete crack detection algorithm

This study uses a novel directional lighting approach to produce a computationally efficient five-channel Visual Geometry Group-16 (VGG-16) convolutional neural network (CNN) model for concrete crack detection and classification in low-light environments. The first convolutional layer of the proposed model copies the weights for the first three channels from the pre-trained model. In contrast, the additional two channels are set to the average of the existing weights along the channels. The model employs transfer learning and fine-tuning approaches to enhance accuracy and efficiency. It utilizes variations in patterned lighting to produce five channels. Each channel represents a grayscale version of the images captured using directed lighting in the right, below, left, above, and diffused directions, respectively. The model is evaluated on concrete crack samples with crack widths ranging from 0.07 mm to 0.3 mm. The modified five-channel VGG-16 model outperformed the traditional three-channel model, showing improvements ranging from 6.5 to 11.7 percent in true positive rate, false positive rate, precision, F1 score, accuracy, and Matthew’s correlation coefficient. These performance improvements are achieved with no significant change in evaluation time. This study provides useful information for constructing custom CNN models for civil engineering problems. Furthermore, it introduces a novel technique to identify cracks in concrete buildings using directed illumination in low-light conditions

3D reconstruction and measurement of concrete spalling using near-field photometric stereo and YOLOv8

Current concrete spalling detection and measurement methods are sparse; despite recent research and commercial offerings using laser scanners, manual measurement is still the industry standard. This paper presents a spalling 3D reconstruction and measurement method. The method uses images illuminated with angled and directional lighting and three neural networks for Photometric stereo 3D mesh generation and spalling volume measurement. The proposed method was benchmarked on a laboratory dataset of spalled concrete slabs against a high-resolution laser scanner, yielding an average height error of 0.0 mm and a standard deviation of 1.3 mm. Volume comparisons showed that with manual input, the method achieved a mean absolute percentage error of 22%. Finally, the proposed technique was compared to manual measurements and benchmarked on a spalled concrete structure against a Trimble X12 laser scanner. This research can provide inspectors with increased data interpretability and reduced imaging time for concrete defect mapping

A novel directional lighting algorithm for concrete crack pixel-level segmentation

External lighting is required for autonomous inspections of concrete structures in low-light environments; however, previous studies have only considered uniformly diffused lighting to illuminate images. This study proposes a novel algorithm that utilises angled and directional lighting to obtain pixel-level segmentation of concrete cracks. The method applies a concrete crack detection algorithm to separate images, each illuminated with lighting from a different direction. Using a bitwise OR operation, the findings from all images are combined; the resulting image highlights the extremities of any present cracks in all lighting directions. When tested on a dataset of cracks ranging in widths from 0.07 mm to 0.3 mm, the algorithm obtained recall, precision and F1 score results of 77%, 84% and 92%, respectively. The algorithm was able to correctly segment cracks that were deemed too thin for similar diffused lighting segmentation methods found in literature. The proposed directional lighting algorithm has the potential to improve concrete inspections in low-light environments