Crack Recognition and Segmentation Using Morphological Image-Processing Techniques for Flexible Pavements

Yun, Hae‐Bum; Mokhtari, Soroush; Wu, Liuliu

doi:10.3141/2523-13

Cited by 26 publications

(14 citation statements)

References 5 publications

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“…Morphological image processing techniques are often used to connect crack fragments into crack curves and for complicated crack shapes in image-based methods [ 2 , 37 ]. Unlike the geometric grid, the Euclidean distance of the adjacent points on the Tgrid is constantly changing.…”

Section: Methodsmentioning

confidence: 99%

Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid

Zhong

Sui

Wang

et al. 2020

Sensors

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This paper presents a novel algorithm for detecting pavement cracks from mobile laser scanning (MLS) data. The algorithm losslessly transforms MLS data into a regular grid structure to adopt the proven image-based methods of crack extraction. To address the problem of lacking topology, this study assigns a two-dimensional index for each laser point depending on its scanning angle or acquisition time. Next, crack candidates are identified by integrating the differential intensity and height changes from their neighbors. Then, morphology filtering, a thinning algorithm, and the Freeman codes serve for the extraction of the edge and skeleton of the crack curves. Further than the other studies, this work quantitatively evaluates crack shape parameters: crack direction, width, length, and area, from the extracted crack points. The F1 scores of the quantity of the transverse, longitudinal, and oblique cracks correctly extracted from the test data reached 96.55%, 87.09%, and 81.48%, respectively. In addition, the average accuracy of the crack width and length exceeded 0.812 and 0.897. Experimental results demonstrate that the proposed approach is robust for detecting pavement cracks in a complex road surface status. The proposed method is also promising in serving the extraction of other on-road objects.

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Section: Methodsmentioning

confidence: 99%

Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid

Zhong

Sui

Wang

et al. 2020

Sensors

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“…Based on the crack images, image processing techniques (IPTs) are often used for crack detection, especially in earlier studies. Basically, due to their lower intensity in the gray-scale image than the background, cracks can be directly extracted by thresholds [23], [24] or by using morphological bottom-hat transform to reduce the effect of the nonuniform illumination [25]. Moreover, as edge-like features that cracks take, edge detection and texture feature extraction algorithms are introduced to obtain the profiles of cracks.…”

Section: B the Development Of Crack Detection Techniquesmentioning

confidence: 99%

Machine Vision-Based Monitoring Methodology for the Fatigue Cracks in U-Rib-to-Deck Weld Seams

et al. 2020

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The orthotropic steel-box girder (OSG) is widely used in the construction of a large-scale bridges. Since cumulative damages caused by the heavy vehicles and initial flaws of welding, the bridges with OSGs frequently suffer from fatigue cracks, which are commonly distributed around U-ribs. Hence, the management of fatigue cracks is mandatory in practical engineering. Although some techniques have been adopted for the detection of cracks, the workflow is often labor-intensive, time-consuming, and of low-temporal resolution. Considering the optical visibility of a crack and the limitation of the shape of an over-welding-hole around the U-rib, a machine vision-based monitoring methodology for the fatigue cracks in U-rib-to-deck weld seams is proposed in this paper. To be specific, a specific Internet of Things (IoT) based image acquisition device is first developed and introduced to obtain precisely part-view images of a fatigue crack. As followed, a novel image rectification and stitching method based on a specified coded calibration board is innovated and described for generating a measurable panoramic fatigue crack image. Furthermore, a deep learning-based crack detection-segmentation integrated algorithm is developed to detect and segment the crack areas. Afterwards, a feature extraction procedure based on image processing is explored to obtain the morphological features of a crack, involving its area, length and width. Finally, a field experiment was carried out on a real steel suspension bridge. By comparing the measurements both from manual measuring and vision-based monitoring, the results indicate that the proposed methodology is very promising to monitor the fatigue cracks in U-rib-to-deck weld seams, and the root-mean-square errors in length and width measuring could be 3.0195 mm and 0.003 mm, respectively. This work is not only of practical value to the management and maintenance of the OSG bridges in engineering, but also critical for the researches on fatigue cracks propagation. INDEX TERMS Orthotropic steel deck, machine vision, IoT based image acquisition system, fatigue crack measuring, image stitching, deep learning. DALEI WANG received the Ph.D. degree in bridge and tunnel engineering from Tongji University, China, in 2012. He is currently the Head of the Research Group of Computer Vision and Artificial Intelligence Application in Bridge Engineering, and is an Associate Professor and the Assistant Dean of the College of Civil Engineering, Tongji University. He has authored over 30 international refereed journals and conference papers, and holds eight patents. His current research interests include computer vision, machine learning, and artificial intelligence and their applications to the monitoring, evaluation, and maintenance of long-span bridges.

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“…Automated crack detection with computer vision method is an effective way to replace manual inspection. In the past, researchers often adopted image processing, edge detection, and morphological operations to detect crack [3][4][5][6][7][8][9]. For dam concrete, Fan Xinnan et al [10] uses localglobal clustering analysis and the image processing method to identify the visual detection of underwater dam surface cracks.…”

Section: Introductionmentioning

confidence: 99%

An Improved Nondestructive Semantic Segmentation Method for Concrete Dam Surface Crack Images with High Resolution

Zhang

2020

Mathematical Problems in Engineering

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To nondestructive semantic segment the crack pixels in the image with high resolution, previous methods often use sliding window and the crack patches to train the FCNs, and then use the trained FCNs for crack recognition. However, the FCNs will produce a higher proportion of false crack predictions with messy distributions in the high-resolution image. A CNN-to-FCN method is proposed to solve this problem. The CNN is trained by all the patches for large-scale crack and background recognition, and the screened crack predictions are then segmented by the FCN. A real-world concrete dam surface crack image database is firstly established to verify the improved method. The results indicated that (1) the improved method can extremely avoid the higher proportion of false crack predictions and their messy distributions in the high-resolution image through the full utilization of background patches and large-scale background recognition; (2) the ResNetv2 backbone and DeepLabv3 architecture recommended by the improved method can be further modified by reducing the bottleneck channels and adding a DUC module to achieve better performance; (3) the improved method can also reduce the prediction time when the image has low proportion of crack patches, which becomes more practicable for the engineering applications.

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Crack Recognition and Segmentation Using Morphological Image-Processing Techniques for Flexible Pavements

Cited by 26 publications

References 5 publications

Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid

Pavement Crack Detection from Mobile Laser Scanning Point Clouds Using a Time Grid

Machine Vision-Based Monitoring Methodology for the Fatigue Cracks in U-Rib-to-Deck Weld Seams

An Improved Nondestructive Semantic Segmentation Method for Concrete Dam Surface Crack Images with High Resolution

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