Automatic concrete cracks detection and mapping of terrestrial laser scan data

Karayolu yüzeyindeki asfalt çatlakları en yaygın kaplama hasarlarından biridir. Bu çatlaklar zamanında tespit edilip bakıma alınmazsa büyüyerek trafik güvenliğini ve yoğunluğunu arttıracak boyutlara ulaşmaktadır. Bu nedenle, asfalt çatlaklarının tespitinin hızlı bir şekilde gerçekleştirilmesi çok önemlidir. Geleneksel manuel çatlak tespiti, son derece zaman alıcı, çok maliyetli ve çok çaba gerektirir. Bu nedenle, araştırmacılar odaklarını otomatik asfalt çatlaklarının tespitine yoğunlaştırdılar. Ancak, son yıllarda otomatik çatlak tespiti kapsamlı olarak araştırılmasına rağmen çatlakların farklı yoğunluğa sahip olması ve kaplama ortamının karmaşıklığı nedeniyle hala zor bir görevdir. Bu çalışmada, bu zorluğun üstesinden gelmek için konvolüsyon sinir ağı temelli bir yöntem önerildi. Önerilen yöntem, sınıflandırma ve segmentasyondaki başarısı ve hafifliği bilinen MobileNetv2 ’nin temelde kullandığı konvolüsyon ve ters artıklı blok yapılarını baz alarak geliştirildi. Yapılan deneysel testler sonucunda, önerilen yöntemin performansı literatürdeki diğer yöntemlere göre daha yüksek olduğu görülmektedir. Bu da, otomatik asfalt çatlak tespitinin daha başarılı olması anlamına gelmektedir.

Automatic crack detection is a main task in a crack map generation of the existing concrete infrastructure inspection. This paper presents an automatic crack detection and classification method based on genetic algorithm (GA) to optimize the parameters of image processing techniques (IPTs). The crack detection results of concrete infrastructure surface images under various complex photometric conditions still remain noise pixels. Next, a deep convolution neural network (CNN) method is applied to classify crack candidates and non-crack candidates automatically. Moreover, the proposed method is compared with the state-of-the-art methods for crack detection. The experimental results validate the reasonable accuracy in practical application.

In the practical applications of crack detection, traditional image processing based detection methods are faced with a lot of challenges, such as low level of intelligence, poor adaptability and so on. In this paper, an automatic pixel-level crack detection method based on deep transfer learning is proposed for engineering applications. The proposed detection method is composed of two stages: crack recognition and crack semantic segmentation, which makes it easy to meet the needs of efficient and reliable detection for large-scale collected images. The fine-tuned Vgg16 model in the first stage can accurately identify the crack images and avoid the computing cost for non-crack images in the further processing. The Unet++ model in the second stage provides a pixel-level semantic segmentation for crack images. Multifold knowledge based transfer learning and weighted loss function are proposed to improve the performance and generalization ability of the models. On 5 publicly available datasets, the proposed method achieved a mIoU of 84.62%. Tested on totally 8064 images with a resolution of 224×224, the proposed method achieved a mean detection speed of 8.1ms per image. Experiment results exhibit that the proposed method can effectively detect the pixel-level cracks. It provides an end-to-end deep learning method for large-scale crack detection for civil infrastructure. For the crack detection in complex scenes and the improvement of processing speed will be our next in-depth study.

Crack detection using image processing: A critical review and analysis

For some time past, terrestrial laser scanning has been adopted as one of the data acquisition techniques for e.g. deformation measurements, documentation of historical monuments and civil engineering projects. Using terrestrial laser scanning, millions of 3D points can be obtained with a high accuracy in a time span of minutes or even seconds depending on the type of laser scanner. However, processing the data still remains a time consuming process. As a result, total station measurements are often preferred over laser scanning for high frequency deformation measurements when time for data processing is limited. In the research at hand, the applicability of terrestrial laser scanning in time critical situations was assessed based on the case of a recent monitoring project on a sewage purification plant. Deformations had to be assessed twice a day in order to prevent accidents from happening. The total station measurements were executed and processed by a specialized company (Teccon bvba, Belgium). At the same time, terrestrial laser scanning was used by researchers of University College Ghent and Ghent University to acquire deformation data. The data obtained by laser scanning was processed independently and then georeferenced to the same coordinate system as used for the total station measurements to enable comparison of the resulting deformations. In order to be able to meet the time requirements, a “quick and dirty” method was developed to process the laser scan data. This method implied no cleaning up of the point clouds and only primitive modeling of parts of the structure. Although a complete 3D model of the whole structure could not be generated in the limited time frame between two scans, the objective, namely an accurate assessment of deformations almost in real time, was accomplished. Furthermore, the degree of detail that can be reached through the use of laser scanning surpasses the possibilities of total stations. Moreover, even with a “quick and dirty method”, visualization is much more comprehensive than can be obtained by using a total station.

Automatic road crack detection can play an significant role in improving driving safety. However, a number of factors make this task challenging. Background complexity, as well as the fact that cracks are visually inhomogenous to one another also contribute to this difficulty. Furthermore, cracks in road surfaces can be easily confused with foreign objects, shadows and background textures leading to detection ambiguities. Timely detection of cracks is important for both drivers and road maintenance crews. Quaternion neural networks (QNNs) are a relatively new class of neural networks that employ quaternionvalued activations and parameters. They benefit from reduced costs in terms of hardware as they require fewer parameters. In this work, we explore the usability of QNNs for automatic road crack detection. To this end, we propose quaternionic versions of deep networks and evaluate their performance in datasets with images of road cracks. We show that the proposed models are light-weight in terms of parameter requirements while they are on par in terms of performance with real-valued networks for crack detection, with potential applications in resource constrained scenarios or in cases where few training data is available.

Automatic edge detection of ply cracks in glass fiber composite laminates under quasi-static and fatigue loading using multi-scale Digital Image Correlation

Detecting cracks on concrete surface images is a key inspection for maintaining infrastructures such as bridge and tunnels. From the viewpoint of computer vision, the task of automatic crack detection poses two challenges. First, since the cracks are visually depicted by subtle patterns and also exhibit similar appearance to the other structural patterns, it is difficult to discriminatively characterize such less distinctive and finer defects. Second, the cracks are scarcely found, making the number of training samples for cracks significantly smaller than that of the other normal samples to be distinguished from the cracks. This is regarded as a class imbalance problem where the classifier is highly biased toward majority classes. In this study, we propose two methods to address these issues in the framework of deep learning for crack detection: a novel network, called Spiral-Net, and an effective optimization method to train the network. The proposed network is extended from U-Net to extract more detailed visual features, and the optimization method is formulated based on F1 score (F-measure) for properly learning the network even on the highly imbalanced training samples. The experimental results on crack detection demonstrate that the two proposed methods contribute to performance improvement individually and jointly.

Automatic surface crack detection using segmentation-based deep-learning approach

Abstract. Civil infrastructure Structural Health Monitoring (SHM) and its preservation from deterioration is a crucial task. In general, natural disasters like severe earthquakes, extreme landslides, subsidence or intensive floods directly influence the health of civil structures such as buildings, bridges, roads, and dams. Evaluation and inspection of defects and damages of the aforementioned structures help to preserve them from destruction by accelerating rehabilitation and reconstruction. An automatic and precise crack detection framework is required for periodic assessment and inspection due to the large number of the structures. In this study, a two-step crack segmentation and its 3D reconstruction procedure is proposed. The crack segmentation is carried out by using Deeplabv3+ architecture and Xception as the backbone. Next, Squeeze-and-Excitation is added as an attention module to achieve higher accuracy. Integration of predicted masks and original images into a structure-from-motion procedure is additionally taken into account. In the last step, ground control points and scale bars are considered to overcome the problem of datum rank deficiency in absolute orientation through the bundle adjustment procedure in aerial triangulation. The most probable segmented cracks are overlaid on the 3D point clouds in the global coordinate system with true scales. Our network is trained based on 8000 images and their corresponding masks, leading to 69% in Intersection over Union (IoU) index. Sub-millimetre accuracy of crack reconstruction using the proposed methodology is validated with a scale bar.

Automatic real-time crack detection using lightweight deep learning models

A novel approach of surface crack detection using super pixel segmentation

Automatic pavement cracking detection is a part of road maintenance and rehabilitation strategies. Cracks detection is one of the main features used by road authorities to manage efficiently its networks. Different systems are available to perform road analysis. We give a short description of some of them. Apparatus which was used to provide our images is described with more details. Road surface is made using randomly organized aggregates which can have different sizes. Scanned pictures of theses surfaces appear as random distribution of a reduced set of gray levels. Automatic crack detection is a difficult task due to the noisy pavement surface. In this paper, we introduce a measure of anisotropy for the characterization of cracks. The basic idea of this method is to detect the variation of features by considering different orientations. Noise variation and defect properties can be take into account by our method. Comparative results of anisotropy method with threshold method and 2D wavelet transform method are presented to illustrate benefits of anisotropy. We show that this method can be used to detect others types of defects, such as joints.

Dynamically developing terrestrial laser scanning technology (TLS)provides modern surveying tools, that is, scanning total stations and laser scanners. Owing to these instruments, periodic control surveys of concrete dams were performed as a part of geodetic monitoring yield point models characterised by quasicontinuity. Using the results of such measurements as a base, one can carry out a number of geometric analyses as well as acquire information for detailed analytical and calculative considerations. The scanner, similar to total station, by determining distances and angles, identifies spatial coordinates (X, Y, Z) of the surveyed points. Registration of the reflected laser beam’s intensity value (Intensity) emitted by the scanner provides additional information on the surveyed object. Owing to high working speed and the large amount of the collected data, the scanners became an indispensable tool for geodesists. The article assesses the possibility of application of terrestrial laser scanning in surveying changes in the surface of a concrete dam based on the experimental measurements. The condition of the dam’s downstream concrete wall was evaluated. The evaluation included changes in the surface’s roughness, cracks, seepage points, erosion caused by plant overgrowth and the degressive durability parameter of the used material (concrete). The article presents an example of the application of the results of a laser scan in the assessment of the condition of a water dam’s external concrete surfaces. The results of experimental measurements were analysed – the results of a scan of the downstream concrete wall of a dam in Ecker (Germany) using two laser scanners characterised by different technical parameters, that is, laser wavelength (laser’s colour), range, definable point density, method of distance measurement – Leica C10, Z+F Imager 5006h. The measurement was carried out in the same weather conditions from the same sites of the test base. The results of the measurements were analysed using, inter alia, statistical methods by defining template fields and supervised and unsupervised classification methods in reference to the selected fragments of the surface characterised by known concrete surface properties. Various classification algorithms were used. The obtained results make it possible to assess the suitability of the proposed methodology of evaluating the concrete surface’s condition and establish tool selection principles to match the practical application requirements.

Fiber-reinforced concrete is a crucial material for civil construction, and monitoring its health is important for preserving structures and preventing accidents and financial losses. Among non-destructive monitoring methods, Micro Computed Tomography (Micro-CT) imaging stands out as an inexpensive method that is free from noise and external interference. However, manual inspection of these images is subjective and requires significant human effort. In recent years, several studies have successfully utilized Deep Learning models for the automatic detection of cracks in concrete. However, according to the literature, a gap remains in the context of detecting cracks using Micro-CT images of fiber-reinforced concrete. Therefore, this work proposes a framework for automatic crack detection that combines the following: (a) a super-resolution-based preprocessing to generate, for each image, versions with double and quadruple the original resolution, (b) a classification step using EfficientNetB0 to classify the type of concrete matrix, (c) specific training of Detection Transformer (DETR) models for each type of matrix and resolution, and (d) and a votation committee-based post-processing among the models trained for each resolution to reduce false positives. The model was trained on a new publicly available dataset, the FIRECON dataset, which consists of 4064 images annotated by an expert, achieving metrics of 86.098% Intersection over Union, 89.37% Precision, 83.26% Recall, 84.99% F1-Score, and 44.69% Average Precision. The framework, therefore, significantly reduces analysis time and improves consistency compared to the manual methods used in previous studies. The results demonstrate the potential of Deep Learning to aid image analysis in damage assessments, providing valuable insights into the damage mechanisms of fiber-reinforced concrete and contributing to the development of durable, high-performance engineering materials.