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Zixian Wei, , Yuhao Wu, Liqing Zhou, Xiaoli Ruan
IET Image Processing; https://doi.org/10.1049/ipr2.12393

Abstract:
Crack is a common form of road distress and a key study of an intelligent transportation system. However, automatic pavement crack detection is a very challenging task due to noisy texture background, intensity inhomogeneity, and topology complexity. In this paper, a new pavement crack detection algorithm to address these issues is proposed. First, non-local block matching strategy and local statistical mean are put together to generate the probability map of cracks, which has advantages on automatic threshold choosing and strong resistance to intensity inhomogeneity. Second, an iterative seed points sampling algorithm is proposed, which makes full use of the area and shape of connected regions where the seeds lie in, thus exploiting high reliable crack seeds for following curves extraction. Finally, a minimum spanning tree (MST) is adopted to connect points into crack curves and employ a crack growth method to find out the cracks, which is specified to deal with complex topology of cracks. For parameters, a robust and optimal parameters selection rule is obtained by data driven method. The algorithm is compared with other state-of-the-art algorithms on two datasets. The experiment result shows that the proposed method has a better detection performance on F1-measure score over other methods.
Published: 29 July 2021
by MDPI
Sensors, Volume 21; https://doi.org/10.3390/s21155137

Abstract:
Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in shapes and sizes, demanding a better incorporation of contextual information into deep networks. In this paper, we show that an attention-based multi-scale convolutional neural network (A+MCNN) improves the automated classification of common distress and non-distress objects in pavement images by (i) encoding contextual information through multi-scale input tiles and (ii) employing a mid-fusion approach with an attention module for heterogeneous image contexts from different input scales. A+MCNN is trained and tested with four distress classes (crack, crack seal, patch, pothole), five non-distress classes (joint, marker, manhole cover, curbing, shoulder), and two pavement classes (asphalt, concrete). A+MCNN is compared with four deep classifiers that are widely used in transportation applications and a generic CNN classifier (as the control model). The results show that A+MCNN consistently outperforms the baselines by 1∼26% on average in terms of the F-score. A comprehensive discussion is also presented regarding how these classifiers perform differently on different road objects, which has been rarely addressed in the existing literature.
Gang Li, Yongqiang Chen, Jian Zhou, Xuan Zheng, Xue Li
Published: 1 July 2021
Engineering Computations; https://doi.org/10.1108/ec-01-2021-0043

Abstract:
Purpose Periodic inspection and maintenance are essential for effective pavement preservation. Cracks not only affect the appearance of the road and reduce the levelness, but also shorten the life of road. However, traditional road crack detection methods based on manual investigations and image processing are costly, inefficiency and unreliable. The research aims to replace the traditional road crack detection method and further improve the detection effect. Design/methodology/approach In this paper, a crack detection method based on matrix network fusing corner-based detection and segmentation network is proposed to effectively identify cracks. The method combines ResNet 152 with matrix network as the backbone network to achieve feature reuse of the crack. The crack region is identified by corners, and segmentation network is constructed to extract the crack. Finally, parameters such as the length and width of the cracks were calculated from the geometric characteristics of the cracks and the relative errors with the actual values were 4.23 and 6.98% respectively. Findings To improve the accuracy of crack detection, the model was optimized with the Adam algorithm and mixed with two publicly available datasets for model training and testing and compared with various methods. The results show that the detection performance of our method is better than many excellent algorithms, and the anti-interference ability is strong. Originality/value This paper proposed a new type of road crack detection method. The detection effect is better than a variety of detection algorithms and has strong anti-interference ability, which can completely replace traditional crack detection methods and meet engineering needs.
Yishun Li, Pengyu Che, Chenglong Liu, Difei Wu, Yuchuan Du
Computer-Aided Civil and Infrastructure Engineering, Volume 36, pp 1398-1415; https://doi.org/10.1111/mice.12674

The publisher has not yet granted permission to display this abstract.
Published: 21 April 2021
by MDPI
Sensors, Volume 21; https://doi.org/10.3390/s21092902

Abstract:
Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.
Yanyan Wang, Kechen Song, Jie Liu, Hongwen Dong, Yunhui Yan, Peng Jiang
Published: 10 November 2020
The publisher has not yet granted permission to display this abstract.
Shanglian Zhou,
Published: 25 August 2020
Structural Health Monitoring, Volume 20, pp 1274-1293; https://doi.org/10.1177/1475921720948434

Abstract:
By providing accurate and efficient crack detection and localization, image-based crack detection methodologies can facilitate the decision-making and rehabilitation of the roadway infrastructure. Deep convolutional neural network, as one of the most prevailing image-based methodologies on object recognition, has been extensively adopted for crack classification tasks in the recent decade. For most of the current deep convolutional neural network–based techniques, either intensity or range image data are utilized to interpret the crack presence. However, the complexities in real-world data may impair the robustness of deep convolutional neural network architecture in its ability to analyze image data with various types of disturbances, such as low contrast in intensity images and shallow cracks in range images. The detection performance under these disturbances is important to protect the investment in infrastructure, as it can reveal the trend of crack evolution and provide information at an early stage to promote precautionary measures. This article proposes novel deep convolutional neural network–based roadway classification tools and investigates their performance from the perspective of using heterogeneous image fusion. A vehicle-mounted laser imaging system is adopted for data acquisition (DAQ) on concrete roadways with a depth resolution of 0.1 mm and an accuracy of 0.4 mm. In total, four types of image data including raw intensity, raw range, filtered range, and fused raw image data are utilized to train and test the deep convolutional neural network architectures proposed in this study. The experimental cases demonstrate that the proposed data fusion approach can reduce false detections and thus results in an improvement of 4.5%, 1.2%, and 0.7% in the F-measure value, respectively, compared to utilizing the raw intensity, raw range, and filtered range image data for analysis. Furthermore, in another experimental case, two novel deep convolutional neural network architectures proposed in this study are compared to exploit the fused raw image data, and the one leading to better classification performance is determined.
Published: 2 July 2020
by MDPI
Materials, Volume 13; https://doi.org/10.3390/ma13132960

Abstract:
Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.
Published: 17 March 2020
by MDPI
Remote Sensing, Volume 12; https://doi.org/10.3390/rs12060968

Abstract:
Routine maintenance of drainage systems, including structure inspection and dredging, plays an essential role in disaster prevention and reduction. Autonomous systems have been explored to assist in pipeline inspection due to safety issues in unknown underground environments. Most of the existing systems merely rely on video records for visual examination since sensors such as a laser scanner or sonar are costly, and the data processing requires expertise. This study developed a compact platform for sewer inspection, which consisted of low-cost components such as infrared and depth cameras with a g-sensor. Except for visual inspection, the platform not only identifies internal faults and obstacles but also evaluates their geometric information, geo-locations, and the block ratio of a pipeline in an automated fashion. As the platform moving, the g-sensor reflects the pipeline flatness, while an integrated simultaneous localization and mapping (SLAM) strategy reconstructs the 3D map of the pipeline conditions simultaneously. In the light of the experimental results, the reconstructed moving trajectory achieved a relative accuracy of 0.016 m when no additional control points deployed along the inspecting path. The geometric information of observed defects accomplishes an accuracy of 0.9 cm in length and width estimation and an accuracy of 1.1% in block ratio evaluation, showing promising results for practical sewer inspection. Moreover, the labeled deficiencies directly increase the automation level of documenting irregularity and facilitate the understanding of pipeline conditions for management and maintenance.
Jing Yang, , Mingxin Nie
IOP Conference Series: Materials Science and Engineering, Volume 782; https://doi.org/10.1088/1757-899x/782/4/042033

Abstract:
Cracks are common pavement diseases that affect pavement performance. To maintain the road in good condition, localizing and fixing the cracks is a vital responsibility for transportation maintenance department. However, traditional manual detection methods are considerably tedious and require domain expertise. Therefore, the research on automatic detection and identification of pavement crack is of great significance for ensuring traffic safety and pavement maintenance decisions. In this paper, we propose an automatic pavement crack detection network based on the Single Shot MultiBox Detector(SSD) deep learning framework, and introduce the receptive field module to enhance the feature extraction capability of the network, which ensures real-time crack detection and also improves the performance of accuracy in pavement crack detection.
Published: 28 January 2020
by MDPI
Sensors, Volume 20; https://doi.org/10.3390/s20030717

Abstract:
Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496 × 496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder–decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it’s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from −11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.
Ju Huyan, , Susan Tighe, Ranran Deng, Shuai Yan
Journal of Computing in Civil Engineering, Volume 34; https://doi.org/10.1061/(asce)cp.1943-5487.0000869

Abstract:
A small amount of research has been conducted in dealing with pavement shadow–affected cracks detection. Hence, this research aims to develop the illumination compensation model (ICM) and k -means clustering algorithm–based crack detection considering the influence of pavement shadows. First, the shadow area was divided into the umbra area and the penumbra area according to the illumination mechanism. Then, the shadow removal methods for different areas were analyzed separately. Since the intensity of the umbra shadow area changes homogeneously, the ICM approach can be a convenient way for shadow removal. While the intensity of penumbra area changes drastically, the cubic sample interpolation operation was conducted in advance, followed by ICM to finalize the shadow removal. After that, the k -means clustering algorithm was used to extract the crack region from the road background. Finally, based on the segmented binary crack image, the orientation, crack length, width, aspect ratio, area, and blocks were calculated for comprehensive crack-type classification and severity evaluation. Experiments were conducted to compare the performance of the proposed approach with traditional threshold segmentation, Poisson equation, contourlet transformation, and CrackTree, which demonstrated optimistic performance of the proposed method in terms of average precision (93.58%), recall (94.15%), and F-measure (93.86%).
Huy Toan Nguyen, Gwang Hyun Yu, Seung You Na, Jin Young Kim, Kyung Sik Seo
The Journal of Korean Institute of Information Technology, Volume 17, pp 99-112; https://doi.org/10.14801/jkiit.2019.17.9.99

, , Xiaohu Lu, Renping Xie, Li Li
Published: 22 January 2019
Neurocomputing, Volume 338, pp 139-153; https://doi.org/10.1016/j.neucom.2019.01.036

The publisher has not yet granted permission to display this abstract.
Published: 5 December 2018
by MDPI
Materials, Volume 11; https://doi.org/10.3390/ma11122467

Abstract:
The research of fractographic images of metals is an important method that allows obtaining valuable information about the physical and mechanical properties of a metallic specimen, determining the causes of its fracture, and developing models for optimizing its properties. One of the main lines of research in this case is studying the characteristics of the dimples of viscous detachment, which are formed on the metal surface in the process of its fracture. This paper proposes a method for detecting dimples of viscous detachment on a fractographic image, which is based on using a convolutional neural network. Compared to classical image processing algorithms, the use of the neural network significantly reduces the number of parameters to be adjusted manually. In addition, when being trained, the neural network can reveal a lot more characteristic features that affect the quality of recognition in a positive way. This makes the method more versatile and accurate. We investigated 17 models of convolutional neural networks with different structures and selected the optimal variant in terms of accuracy and speed. The proposed neural network classifies image pixels into two categories: “dimple” and “edge”. A transition from a probabilistic result at the output of the neural network to an unambiguously clear classification is proposed. The results obtained using the neural network were compared to the results obtained using a previously developed algorithm based on a set of filters. It has been found that the results are very similar (more than 90% similarity), but the neural network reveals the necessary features more accurately than the previous method.
Lei Huang, Vignesh Mohanraj, Hossein Asghari
2018 International Conference on Image and Video Processing, and Artificial Intelligence, Volume 10836; https://doi.org/10.1117/12.2504606

Abstract:
Automatic road crack detection using image/video data plays a crucial role in the maintenance of road service life and the improvement of driving experiences. In this paper, an improved automatic road crack detection system is proposed to reduce false detection under various noisy road surface conditions and to improve sensitivity in detecting light and thin cracks. The proposed system combines a variety of traditional image processing techniques, such as filtering and morphological processing, with scalable and efficient machine learning algorithms. Real road images with various noise conditions are taken to evaluate the performance of the proposed system. Experimental results have shown that the proposed system improved detection sensitivity and reduced false detection compared to some existing system, thus achieving higher detection accuracy.
Baoxian Li, , Allen Zhang, Enhui Yang, Guolong Wang
International Journal of Pavement Engineering, Volume 21, pp 457-463; https://doi.org/10.1080/10298436.2018.1485917

Abstract:
The classification of pavement crack heavily relies on the engineers’ experience or the hand-crafted algorithms. Convolutional Neural Network (CNN) has demonstrated to be useful for image classification, which provides an alternative to traditional imaging classification algorithms. This paper proposes a novel method using deep CNN to automatically classify image patches cropped from 3D pavement images. In all, four supervised CNNs with different sizes of receptive field are successfully trained. The experimental results demonstrate that all the proposed CNNs can perform the classification with a high accuracy. Overall classification accuracy of each proposed CNN is above 94%. Upon the evaluation of these neural networks with respect to accuracy and training time, we find that the size of receptive field has a slight effect on the classification accuracy. However, the CNNs with smaller size of receptive field require more training times than others.
, Mohammad R. Jahanshahi
Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, Volume 10598; https://doi.org/10.1117/12.2296772

Abstract:
Regularly inspecting the components inside nuclear power plants is necessary to ensure their safe performance. Current inspection practices, however, are time consuming, tedious, and subjective that need technicians to watch videos and manually annotate cracks. While an autonomous inspection approach is desirable, state-of-the-art crack detection algorithms cannot perform well since the cracks on nuclear power plant reactors are typically very tiny with low contrast. The existence of scratches, welds, and grind marks on the surface makes the autonomous detection even more challenging. This study introduces a new framework that consists of a convolutional neural network (CNN) based on deep learning, a spatiotemporal registration process, and a Nave Bayes data fusion scheme based on statistics. The proposed framework is evaluated using several inspection videos and achieves 98.3% hit rate against 0.1 false positives per frame where the hit rate is much higher than other state-of-the-art algorithms.
Kaige Zhang, ,
Journal of Computing in Civil Engineering, Volume 32; https://doi.org/10.1061/(asce)cp.1943-5487.0000736

Abstract:
This work focuses on solving two challenging problems in pavement crack detection: (1) noises caused by complicated pavement textures and intensity inhomogeneity cannot be removed effectively, which makes crack extraction difficult; and (2) sealed cracks and cracks with similar intensity and width cannot be separated correctly, which makes data analysis and budgeting inaccurate. Here, a unified crack and sealed crack detection approach is proposed that can detect and separate both cracks and sealed cracks under the same framework. It trains a deep convolutional neural network to preclassify a pavement image into crack, sealed crack, and background regions. A blockwise thresholding method is developed to segment the crack/sealed crack pixels efficiently and effectively. Finally, tensor voting–based curve detection is applied to extract the crack/sealed crack. The proposed approach is validated using 800 images (each 2,000×4,000  pixels ); the experimental results demonstrate that this approach accurately distinguishes cracks from sealed cracks and achieves very good detection performance (recall=0.951 ; precision=0.847 ).
Zhe Lin, Xiaohua Zhao
IET Image Processing, Volume 12, pp 382-388; https://doi.org/10.1049/iet-ipr.2017.0747

Abstract:
Beamlet transform has been widely used for extracting line features from images, which is an excellent multiscale geometric analysis method. However, it has a major drawback that it always performs too slowly due to very much redundant computation. In many application fields, the speed of the original beamlet transform is almost unbearable. To cure the problem, beamlet transform is improved by introducing geometrical flow, which utilises image semantic information in the process of generating beamlets. Besides, to further speed up the algorithm, interesting factor is presented to reduce recursively partitioned boxes. As a result, lots of computation time is saved. Experiments are conducted on various crack images and the results show that the proposed method runs significantly faster than the original beamlet transform. Cracks in an image are detected accurately. Moreover, the proposed method is robustly enough since the performance is hardly affected by crack shape and background texture.
, Manjit Singh Bhamrah, Hardeep Singh Ryait
Foundations of Computational Intelligence Volume 3 pp 93-101; https://doi.org/10.1007/978-981-10-4555-4_7

The publisher has not yet granted permission to display this abstract.
Fu-Chen Chen,
IEEE Transactions on Industrial Electronics, Volume 65, pp 4392-4400; https://doi.org/10.1109/tie.2017.2764844

Abstract:
Regular inspection of nuclear power plant components is important to guarantee safe operations. However, current practice is time consuming, tedious, and subjective, which involves human technicians reviewing the inspection videos and identifying cracks on reactors. A few vision-based crack detection approaches have been developed for metallic surfaces, and they typically perform poorly when used for analyzing nuclear inspection videos. Detecting these cracks is a challenging task since they are tiny, and noisy patterns exist on the components' surfaces. This study proposes a deep learning framework, based on a convolutional neural network (CNN) and a Naïve Bayes data fusion scheme, called NB-CNN, to analyze individual video frames for crack detection while a novel data fusion scheme is proposed to aggregate the information extracted from each video frame to enhance the overall performance and robustness of the system. To this end, a CNN is proposed to detect crack patches in each video frame, while the proposed data fusion scheme maintains the spatiotemporal coherence of cracks in videos, and the Naïve Bayes decision making discards false positives effectively. The proposed framework achieves a 98.3% hit rate against 0.1 false positives per frame that is significantly higher than state-of-the-art approaches as presented in this paper.
Fu-Chen Chen, Mohammad R. Jahanshahi, Rih-Teng Wu, Chris Joffe
Computer-Aided Civil and Infrastructure Engineering, Volume 32, pp 271-287; https://doi.org/10.1111/mice.12256

The publisher has not yet granted permission to display this abstract.
Rabih Amhaz, Sylvie Chambon, Jerome Idier, Vincent Baltazart
IEEE Transactions on Intelligent Transportation Systems, Volume 17, pp 2718-2729; https://doi.org/10.1109/tits.2015.2477675

Abstract:
This paper proposes a new algorithm for automatic crack detection from 2D pavement images. It strongly relies on the localization of minimal paths within each image, a path being a series of neighboring pixels and its score being the sum of their intensities. The originality of the approach stems from the proposed way to select a set of minimal paths and the two postprocessing steps introduced to improve the quality of the detection. Such an approach is a natural way to take account of both the photometric and geometric characteristics of pavement images. An intensive validation is performed on both synthetic and real images (from five different acquisition systems), with comparisons to five existing methods. The proposed algorithm provides very robust and precise results in a wide range of situations, in a fully unsupervised manner, which is beyond the current state of the art.
Lei Zhang, Fan Yang, Yimin Daniel Zhang, Ying Julie Zhu
2016 IEEE International Conference on Image Processing (ICIP) pp 3708-3712; https://doi.org/10.1109/icip.2016.7533052

Abstract:
Automatic detection of pavement cracks is an important task in transportation maintenance for driving safety assurance. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavement and possible shadows with similar intensity. Inspired by recent success on applying deep learning to computer vision and medical problems, a deep-learning based method for crack detection is proposed in this paper. A supervised deep convolutional neural network is trained to classify each image patch in the collected images. Quantitative evaluation conducted on a data set of 500 images of size 3264 χ 2448, collected by a low-cost smart phone, demonstrates that the learned deep features with the proposed deep learning framework provide superior crack detection performance when compared with features extracted with existing hand-craft methods.
Yong Shi, Limeng Cui, , Fan Meng, Zhensong Chen
IEEE Transactions on Intelligent Transportation Systems, Volume 17, pp 3434-3445; https://doi.org/10.1109/tits.2016.2552248

Abstract:
Cracks are a growing threat to road conditions and have drawn much attention to the construction of intelligent transportation systems. However, as the key part of an intelligent transportation system, automatic road crack detection has been challenged because of the intense inhomogeneity along the cracks, the topology complexity of cracks, the inference of noises with similar texture to the cracks, and so on. In this paper, we propose CrackForest, a novel road crack detection framework based on random structured forests, to address these issues. Our contributions are shown as follows: 1) apply the integral channel features to redefine the tokens that constitute a crack and get better representation of the cracks with intensity inhomogeneity; 2) introduce random structured forests to generate a high-performance crack detector, which can identify arbitrarily complex cracks; and 3) propose a new crack descriptor to characterize cracks and discern them from noises effectively. In addition, our method is faster and easier to parallel. Experimental results prove the state-of-the-art detection precision of CrackForest compared with competing methods.
Published: 12 March 2016
Abstract:
Liver cirrhosis is considered as one of the most common diseases in healthcare. The widely accepted technology for the diagnosis of liver cirrhosis is via ultrasound imaging. This paper presents a technique for detecting the cirrhosis of liver through ultrasound images. The region of interest has been selected from these ultrasound images and endorsed from a radiologist. The identification of liver cirrhosis is finally detected through modified local binary pattern and OTSU methods. Experimental results from the proposed method demonstrated its feasibility and applicability for high performance cirrhotic liver identification.
Romulo Goncalves Lins,
IEEE Transactions on Instrumentation and Measurement, Volume 65, pp 583-590; https://doi.org/10.1109/tim.2015.2509278

Abstract:
Crack detection and measurement in civil structures has been a constant field of research. Conventionally, a technician is responsible to detect and measure cracks in the field. In this paper, a system based on machine vision concepts has been developed with the goal to automate the crack measurement process. Using this method with only a single camera installed in a truck or even in a robot, a sequence of images is processed and the crack dimensions are estimated. The experimental results validate the application of the proposed method for real structures.
, Manjit Singh Bhamrah, Hardeep Singh Ryait
2015 2nd International Conference on Recent Advances in Engineering & Computational Sciences (RAECS) pp 1-6; https://doi.org/10.1109/raecs.2015.7453313

Abstract:
Liver cirrhosis is considered as one of the common most diseases in healthcare. The widely accepted technology for the diagnosis is ultrasound imaging. This paper presents such a technique for detecting the cirrhosis of liver through ultrasound images. The region of interest is selected from the ultrasound images that obtained from radiologist and then inspection technique is applied on it. The identification of liver cirrhosis from normal liver is finally detected through modified Local Binary Pattern (LBP) represented as Differential Local Binary Pattern (DLBP). The image intensities value of DLBP image were divided into five discriminating groups which were made by counting pixels of similar gray scale value. Decision of cirrhotic liver is given by the pixel values in all five groups. Experimental results from the proposed method demonstrated its feasibility and applicability for high performance cirrhotic liver identification.
IEEE Transactions on Intelligent Transportation Systems, Volume 17, pp 608-619; https://doi.org/10.1109/tits.2015.2482222

Abstract:
This paper presents a computer vision system whose aim is to detect and classify cracks on road surfaces. Most of the previous works consisted of complex and expensive acquisition systems, whereas we have developed a simpler one composed by a single camera mounted on a light truck and no additional illumination. The system also includes tracking devices in order to geolocalize the captured images. The computer vision algorithm has three steps: hard shoulder detection, cell candidate proposal, and crack classification. First the region of interest (ROI) is delimited using the Hough transform (HT) to detect the hard shoulders. The cell candidate step is divided into two substeps: Hough transform features (HTF) and local binary pattern (LBP). Both of them split up the image into nonoverlapping small grid cells and also extract edge orientation and texture features, respectively. At the fusion stage, the detection is completed by mixing those techniques and obtaining the crack seeds. Afterward, their shape is improved using a new developed morphology operator. Finally, one classification based on the orientation of the detected lines has been applied following the Chain code. Massive experiments were performed on several stretches on a Spanish road showing very good performance.
, Salar Shahini Shamsabadi, Jennifer Dy, Ming Wang, Ralf Birken
Structural Health Monitoring and Inspection of Advanced Materials, Aerospace, and Civil Infrastructure 2015, Volume 9437; https://doi.org/10.1117/12.2084370

Abstract:
Around 3,000,000 million vehicle miles are annually traveled utilizing the US transportation systems alone. In addition to the road traffic safety, maintaining the road infrastructure in a sound condition promotes a more productive and competitive economy. Due to the significant amounts of financial and human resources required to detect surface cracks by visual inspection, detection of these surface defects are often delayed resulting in deferred maintenance operations. This paper introduces an automatic system for acquisition, detection, classification, and evaluation of pavement surface cracks by unsupervised analysis of images collected from a camera mounted on the rear of a moving vehicle. A Hessian-based multi-scale filter has been utilized to detect ridges in these images at various scales. Post-processing on the extracted features has been implemented to produce statistics of length, width, and area covered by cracks, which are crucial for roadway agencies to assess pavement quality. This process has been realized on three sets of roads with different pavement conditions in the city of Brockton, MA. A ground truth dataset labeled manually is made available to evaluate this algorithm and results rendered more than 90% segmentation accuracy demonstrating the feasibility of employing this approach at a larger scale.
Ionut Gheorghe, , , Keith J. Burnham
Advances in Human Error, Reliability, Resilience, and Performance, Volume 330, pp 691-698; https://doi.org/10.1007/978-3-319-08422-0_98

The publisher has not yet granted permission to display this abstract.
Bo Peng, , Cheng Chen
T&DI Congress 2014 pp 543-552; https://doi.org/10.1061/9780784413586.052

Abstract:
2D image-based crack detection has been studied for many years. But we are still short of a robust method, which can generate satisfied results on general cases. 3D pavement images have obvious advantages over 2D images in dealing with disturbances caused by lane markings, sun-light shadows, oil marks and debris. Taking advantages of the state-of-the-art 3D image acquisition technology, this paper proposes a novel crack detection algorithm. It has four steps: First, generate two partly overlapped images with 8×8 pixel grid cells. Second, extract 10 images of crack seeds through symmetry check and grayscale verification. Third, connect scattered seeds by an optimal path searching process considering grayscale values, cell direction and proximity. Finally, 10 detection results from two grid cell images are combined and denoised to obtain final results. Experiments based on 166 test images show that the proposed method can achieve better performance measured by F-measure, which takes both precision and recall rates into consideration.
Journal of Computing in Civil Engineering, Volume 27, pp 743-754; https://doi.org/10.1061/(asce)cp.1943-5487.0000245

Abstract:
Current pavement condition–assessment procedures are extensively time consuming and laborious; in addition, these approaches pose safety threats to the personnel involved in the process. In this study, a RGB-D sensor is used to detect and quantify defects in pavements. This sensor system consists of a RGB color image, and an infrared projector and a camera that act as a depth sensor. An approach, which does not need any training, is proposed to interpret the data sensed by this inexpensive sensor. This system has the potential to be used for autonomous cost-effective assessment of road-surface conditions. Various road conditions including patching, cracks, and potholes are autonomously detected and, most importantly, quantified, using the proposed approach. Several field experiments have been carried out to evaluate the capabilities, as well as the limitations of the proposed system. The global positioning system information is incorporated with the proposed system to localize the detected defects. This approach has the potential to be deployed as a supplementary sensor system in pavement surface–assessment vehicles and reduce the operation cost.
, S. Mathavan, K. Kamal, M. Rahman
16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) pp 2039-2044; https://doi.org/10.1109/itsc.2013.6728529

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. 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. An initial detection precision of up to 95% has been reported in this paper showing a good promise in the proposed method.
Kun Xu, Na Wei, Ronggui Ma
2013 IEEE Third International Conference on Information Science and Technology (ICIST) pp 1281-1284; https://doi.org/10.1109/icist.2013.6747771

Abstract:
The goal of this paper is to develop an algorithm to automatically detect and classify pavement cracks. Firstly, background subset interpolation method is used to adjust the nonuniform background illumination and decrease the shadows influence. The iterated threshold segmentation method, the closing operation and the sequential labeling of connected components are applied sequentially to segment the crack from the background accurately. Projection feature, total sum of crack pixel and distribution density are selected to classify the cracks. Lastly, the main parameters for crack, such as length, width and area are calculated. The results show that the proposed method is efficient.
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