Pavement crack detection

Complex pavement texture and noise impede the effectiveness of existing 3D pavement crack detection methods.

Pavement crack and joint sealing

To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering FO-DPC. Firstly, the 3D data of asphalt pavement are collected, and a 3D image acquisition system is built using Gocator series binocular intelligent sensors. Then, the fruit fly optimisation algorithm is adopted to improve the density peak clustering algorithm.

Clustering analysis that can accurately detect cracks is performed on the height characteristics of the 3D data of the asphalt pavement.

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Finally, the clustering results are projected onto a 2D space and compared with the results of other 2D crack detection methods. Following this comparison, it is established that the proposed algorithm outperforms existing methods in detecting asphalt pavement cracks. Globally, the economic framework and evolving networks of cities depend on road traffic [ 1 ]. The detection of pavement distress is an important part of pavement maintenance.

Crack is one of the critical pavement defects, and crack length measurement is an important aspect of asphalt pavement crack detection. Now, there are already some crack detection methods [ 2 ]. However, most crack measurement techniques have low accuracy and efficiency. Crack detection technology, which provides significant technical support in pavement maintenance, is becoming more advanced. Traditional artificial crack detection methods affect traffic and have low efficiency, strong subjectivity, and low accuracy; they have been unable to meet the growing pavement maintenance needs for a long time [ 3 ].

With the development of computer technology, the emergence of many pavement crack detection technologies based on digital image processing, such as Laplace, Sobel, Prewitt, Roberts, and Canny operators and other edge detection algorithms, has greatly improved pavement crack detection efficiency and accuracy.

However, these detection algorithms are highly sensitive to noise. Other pavement crack detection methods are based on morphology [ 4 — 6 ], threshold segmentation, and other approaches [ 7 ]. These algorithms provide an important reference for improving crack detection accuracy [ 8 ]. Although there are some quite effective 2D crack detection methods [ 9 ], 2D crack detection methods are easily affected by light, shadow, pavement signs, and oil stains.

Therefore, there is growing scholarly interest in the study of 3D detection of asphalt pavement cracks [ 10 ].Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Marques and F. Nunes and P. Correia Published To keep a high road surface quality and road safety, an appropriate maintenance policy needs to be enforced, as soon as cracks start to appear.

Since the traditional way of visually detecting road cracks by a skilled technician is very time consuming, this dissertation presents an automatic solution, therefore increasing the speed and efficiency of road surface pavement analysis and reducing the technician effort and subjectivity of the achieved results. Save to Library. Create Alert.

Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

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pavement crack detection

View 1 excerpt, cites methods.Metrics details. Each year, millions of dollars are invested on road maintenance and reparation all over the world. In order to minimize costs, one of the main aspects is the early detection of those flaws. Different types of cracks require different types of repairs; therefore, not only a crack detection is required but a crack type classification.

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Also, the earlier the crack is detected, the cheaper the reparation is. Once the images are captured, several processes are applied in order to extract the main characteristics for emphasizing the cracks logarithmic transformation, bilateral filter, Canny algorithm, and a morphological filter.

After image preprocessing, a decision tree heuristic algorithm is applied to finally classify the image. It could be implemented in a vehicle traveling as fast as kmh or 81 mph. Road safety is one of the main concerns nowadays. Due to their intensive general use, to keep road pavement in good conditions is a critical point to decrease accidents and, as a direct consequence, to decrease mortal victims.

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Once roads are built, cracks in the asphalt surface may arise due to several different problems. Depending on the severity, those cracks in the road can be aggravated if they are not quickly repaired. The sooner those flaws in the pavement are repaired, the less expensive the repairs are [ 1 ]. After the use of a series of Bayesian ordered logistic models, they conclude that the poor pavement condition increases the severity of multi-vehicle crashes on all kind of roads and also increases the severity of single-vehicle crash on high-speed road.

Almost every modern country has thousands of kilometers of roads. Visual inspection of the roads has been the most common technique for the maintenance of the asphalt. With such large road networks, it is expensive and difficult to correctly maintain so many kilometers of asphalt with the limited resources that any public administration has. Thus, new tools to examine automatically roads in an efficient way should be developed.

There are several crack types, but they are mainly categorized into three groupings: transverse cracks, longitudinal cracks, and alligator or mesh cracks [ 119 ].

Detecting Pavement Cracks Using Drones and Neural Networking

Each type of pavement crack has to be repaired in a different way. Therefore, it is very interesting to be able to detect cracks in the pavement and to be able to classify each of them. Transverse cracks are perpendicular to the centreline of the pavement. They are usually caused by thermal changes. Other causes are asphalt binder hardening or reflexion cracks provoked by other cracks beneath the asphalt surface. Longitudinal cracks have two main causes: fatigue and poor joints.

Fatigue cracks are produced by a continuous over-load due to heavy vehicles. Joints are generally the least dense areas of the pavement. If joints are located in a high stressed region, a crack could appear.Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Personal Sign In. For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy.

Email Address. Sign In. Automatic Pavement Crack Detection by Multi-Scale Image Fusion Abstract: Pavement crack detection from images is a challenging problem due to intensity inhomogeneity, topology complexity, low contrast, and noisy texture background. Traditional learning-based approaches have difficulties in obtaining representative training samples.

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We propose a new unsupervised multi-scale fusion crack detection MFCD algorithm that does not require training data. First, we develop a windowed minimal intensity path-based method to extract the candidate cracks in the image at each scale.

Second, we find the crack correspondences across different scales. Finally, we develop a crack evaluation model based on a multivariate statistical hypothesis test. Our approach successfully combines strengths from both the large-scale detection robust but poor in localization and the small-scale detection detail-preserving but sensitive to clutter. We analyze and experimentally test the computational complexity of our MFCD algorithm. We have implemented the algorithm and have it extensively tested on three public data sets, including two public pavement data sets and an airport runway data set.

Compared with six existing methods, experimental results show that our method outperforms all counterparts. Article :. Date of Publication: 07 August DOI: Need Help?GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Work fast with our official CLI.

pavement crack detection

Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The project is used to share our recent work on pavement crack detection. The pavement crack datasets used in paper, crack detection results on each datasets, trained model, and crack annotation tool are stored in Google Drive and Daidu Yunpan extract code: jviq.

If you think this project is useful for you, feel free to leave a star. Before using the tool, please make sure that in predicted crack map the bright regions are crack, background is black. If you encounter any issue when using our code or model, feel free to contact me fyang temple.

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Failed to load latest commit information. Jun 3, Jun 22, Jun 24, View code.Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems.

However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant.

Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.

Pavement crack detection plays an important role in the field of road distress evaluation [ 1 ]. Traditional crack detection methods depend mainly on manual work and are limited by the following: i they are time consuming and laborious; ii they rely entirely on human experience and judgment. Therefore, automatic crack detection is essential to detect and identify cracks on the road quickly and accurately [ 2 ].

This procedure is a key part of intelligent maintenance systems, to assist and evaluate the pavement distress quality where more continual road status surveys are required.

Over the past decade, the development of high-speed mobile cameras and large-capacity hardware storage devices has made it easier to obtain large-scale road images. Through mobile surveying and mapping technology, integrated acquisition equipment is fixed to the rear of the vehicle roof frame to monitor both the road surface and the surrounding environment.

The images can be acquired by processing and storing pavement surface images that are realized [ 3 ]. Currently, many methods utilize computer vision algorithms to process the collected pavement crack images and then obtain the final maintenance evaluation results [ 4 ]. Automatic crack detection is a very challenging image classification task with the goal of accurately marking crack areas.

Figure 1 shows examples of data acquisition by a mobile pavement inspection vehicle. In a few cases, the cracks have good continuity and obvious contrast, as shown in Figure 1 a. However, in most cases, there is a considerable noise in cracks, which leads to poor continuity and low contrast, as shown in Figure 1 b.

Therefore, automatic crack detection mainly includes the following three challenges. These three difficulties create considerable challenges in pavement crack detection.

pavement crack detection

The recent publications [ 5 — 7 ] assumed that the crack pixels are generally darker than their surroundings and then used the threshold method to extract the crack area.

These methods lack the description of global information and are sensitive to noise. To improve the continuity of crack detection, researchers have attempted to detect cracks by introducing minimal path selection MPS [ 8 — 10 ], minimal spanning trees MSTs [ 11 — 13 ], and crack fundamental elements CFEs [ 14 ].

These methods can partially eliminate noise and improve crack detection continuity. However, only the low-level features can be roughly obtained, some complex high-level crack features may not be presented, and utilized correctly. A randomly structured forest-based method is presented in [ 15 ] to detect cracks automatically.

This method can effectively suppress noise by manually selecting crack features and learning internal structures. Although it improves the recognition speed and accuracy but does not perform well when dealing with complex pavement crack situations.

Therefore, traditional machine learning methods simulate cracks by manually setting color or texture features. In these methods, the features cover only some specific real-world situations. The set of crack features is simplified and idealized, which cannot achieve the robust detection requirements for pavement diseases. In recent years, deep learning methods have been widely used to solve complex problems through hierarchical concepts.

A deep convolutional neural network DCNN has shown great advantages in computer vision tasks, such as image classification [ 16 — 18 ], object detection [ 19 ], and semantic segmentation [ 2021 ].

The DCNN can acquire expressive features at different levels as it consists of several trainable layers [ 22 ]. The rich hierarchical features of DCNN have made great progress in pixel-level semantic segmentation tasks [ 2324 ] and crack detection. In [ 25 ], the AlexNet is used to extract the crack characteristics, and then crack detection is performed based on probability maps.

However, the detailed division of the crack could not be completed.Skip to Main Content.

Efficient pavement crack detection and classification

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pavement crack detection

Email Address. Sign In. Pavement crack detection using the Gabor filter Abstract: Crack is a common form of pavement distress and it carries significant information on the condition of roads. The detection of cracks is essential to perform pavement maintenance and rehabilitation.

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Many of the highways agencies, in different countries, are still employing conventional, costly and very time consuming techniques which involve direct human intervention and assessment. Although automated recognition has been successfully performed for many pavement distresses, crack detection remains, to this date, a topic where reservations exist. A novel approach to automatically distinguish cracks in digital pavement images is proposed in this paper.

The Gabor filter is proven to be a highly potential technique for multidirectional crack detection that was not done previously using the Gabor filter.

Image analysis using the Gabor function is directly related to the mammalian visual perception, hence the choice of this method for crack detection. Results reported in this paper concentrate on pavement images with high levels of surface texture that makes crack detection difficult.

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