Characterize Rail Crack Pattern Through Image Analysis

2020 ◽  
Author(s):  
Ryan DeVine ◽  
Yu Qian ◽  
Yi Wang ◽  
Shaofeng Wang ◽  
Dimitris Rizos
Keyword(s):  
Image Analysis ◽  
Growth Pattern ◽  
Crack Detection ◽  
Fuel Efficiency ◽  
Vertical Direction ◽  
Safety Requirements ◽  
Detection Techniques ◽  
Rail Head ◽  
Length Direction ◽  
Filtering Technique

Abstract Railway provides more than 40% of the freight ton-miles moved in the U.S. each year, surpassing all other modes of transportation. In addition to moving more tonnage farther than other modes, trains have better fuel efficiency than trucks and airplanes due to the low friction between the wheels and the rails. With traffic accumulation, rails will degrade which may lead to different types of defects, including but not limited to spalling, separation, crack, and corrugation. Rail head fissures or surface crack is often associated with rolling fatigue and must be addressed through grinding or other maintenance activities to restore the smooth-running surface. This ensures the riding conforms to operational safety requirements. The growth pattern of rail surface cracks has not been thoroughly understood or well-quantified yet due to the difficulties of rail crack inspection and insufficient data. This paper presents a study that uses image analysis techniques to detect and quantify cracks in images of rail segments that were taken in the field. Various crack detection techniques were tested and compared with visual inspection, including thresholding, edge detection, and bottom-hat filtering. The crack length, direction, and curvature were also quantified with each approach. Cracks were found to grow not perpendicular to the rail head, but with a certain angle from the vertical direction and relatively evenly distributed along the rail. The bottom-hat filtering technique was found to be the best in terms of accuracy among the methods tested in this study. The results from the study fill the gap of the literature by quantitatively characterizing the rail crack growth pattern and helping to identify possible approaches for future autonomous crack detection.

scholarly journals An Automatic Concrete Crack-Detection Method Fusing Point Clouds and Images Based on Improved Otsu’s Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1581
Author(s):  
Xiaolong Chen ◽  
Jian Li ◽  
Shuowen Huang ◽  
Hao Cui ◽  
Peirong Liu ◽  
...  
Keyword(s):  
Crack Detection ◽  
Detection Method ◽  
Multiple Scales ◽  
Point Clouds ◽  
Depth Image ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Concrete Crack ◽  
3D Point Clouds ◽  
2D Images

Cracks are one of the main distresses that occur on concrete surfaces. Traditional methods for detecting cracks based on two-dimensional (2D) images can be hampered by stains, shadows, and other artifacts, while various three-dimensional (3D) crack-detection techniques, using point clouds, are less affected in this regard but are limited by the measurement accuracy of the 3D laser scanner. In this study, we propose an automatic crack-detection method that fuses 3D point clouds and 2D images based on an improved Otsu algorithm, which consists of the following four major procedures. First, a high-precision registration of a depth image projected from 3D point clouds and 2D images is performed. Second, pixel-level image fusion is performed, which fuses the depth and gray information. Third, a rough crack image is obtained from the fusion image using the improved Otsu method. Finally, the connected domain labeling and morphological methods are used to finely extract the cracks. Experimentally, the proposed method was tested at multiple scales and with various types of concrete crack. The results demonstrate that the proposed method can achieve an average precision of 89.0%, recall of 84.8%, and F1 score of 86.7%, performing significantly better than the single image (average F1 score of 67.6%) and single point cloud (average F1 score of 76.0%) methods. Accordingly, the proposed method has high detection accuracy and universality, indicating its wide potential application as an automatic method for concrete-crack detection.

scholarly journals BRIDGE CRACK DETECTION USING MULTI-ROTARY UAV AND OBJECT-BASE IMAGE ANALYSIS

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 311-318 ◽  
Author(s):  
J. Y. Rau ◽  
K. W. Hsiao ◽  
J. P. Jhan ◽  
S. H. Wang ◽  
W. C. Fang ◽  
...  
Keyword(s):  
Image Analysis ◽  
Crack Detection ◽  
Spatial Information ◽  
Local Coordinate System ◽  
Image Processing Technique ◽  
Classification Rule ◽  
Control Points ◽  
Surface Geometry ◽  
Bridge Inspection ◽  
Volume Calculation

Bridge is an important infrastructure for human life. Thus, the bridge safety monitoring and maintaining is an important issue to the government. Conventionally, bridge inspection were conducted by human in-situ visual examination. This procedure sometimes require under bridge inspection vehicle or climbing under the bridge personally. Thus, its cost and risk is high as well as labor intensive and time consuming. Particularly, its documentation procedure is subjective without 3D spatial information. In order cope with these challenges, this paper propose the use of a multi-rotary UAV that equipped with a SONY A7r2 high resolution digital camera, 50 mm fixed focus length lens, 135 degrees up-down rotating gimbal. The target bridge contains three spans with a total of 60 meters long, 20 meters width and 8 meters height above the water level. In the end, we took about 10,000 images, but some of them were acquired by hand held method taken on the ground using a pole with 2–8 meters long. Those images were processed by Agisoft PhotoscanPro to obtain exterior and interior orientation parameters. A local coordinate system was defined by using 12 ground control points measured by a total station. After triangulation and camera self-calibration, the RMS of control points is less than 3 cm. A 3D CAD model that describe the bridge surface geometry was manually measured by PhotoscanPro. They were composed of planar polygons and will be used for searching related UAV images. Additionally, a photorealistic 3D model can be produced for 3D visualization. In order to detect cracks on the bridge surface, we utilize object-based image analysis (OBIA) technique to segment the image into objects. Later, we derive several object features, such as density, area/bounding box ratio, length/width ratio, length, etc. Then, we can setup a classification rule set to distinguish cracks. Further, we apply semi-global-matching (SGM) to obtain 3D crack information and based on image scale we can calculate the width of a crack object. For spalling volume calculation, we also apply SGM to obtain dense surface geometry. Assuming the background is a planar surface, we can fit a planar function and convert the surface geometry into a DSM. Thus, for spalling area its height will be lower than the plane and its value will be negative. We can thus apply several image processing technique to segment the spalling area and calculate the spalling volume as well. For bridge inspection and UAV image management within a laboratory, we develop a graphic user interface. The major functions include crack auto-detection using OBIA, crack editing, i.e. delete and add cracks, crack attributing, 3D crack visualization, spalling area/volume calculation, bridge defects documentation, etc.

scholarly journals Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review

Data ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 28 ◽  
Author(s):  
Kasthurirangan Gopalakrishnan
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
Asset Management ◽  
Network Architecture ◽  
Crack Detection ◽  
Future Research ◽  
Deep Convolutional Neural Networks ◽  
Pavement Distress ◽  
Learning Software

Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images.

Laser ultrasonic monitoring of reversible/irreversible modification of a real crack under photothermal loading

2020 ◽  
pp. 147592172091516
Author(s):  
Chen-Yin Ni ◽  
Jin-Chao LV ◽  
Yue-Ying Zhang ◽  
Hai-Yan He ◽  
Xi-Feng Xia ◽  
...  
Keyword(s):  
Acoustic Waves ◽  
Crack Detection ◽  
Optical Measurement ◽  
Mode Conversion ◽  
Surface Acoustic Waves ◽  
Equilibrium Temperature ◽  
Laser Ultrasonics ◽  
Loading Cycle ◽  
Detection Techniques ◽  
Significant Difference

We study the responses of laser-generated acoustic waves to localized reversible/irreversible modifications of microscopic asperities on crack surfaces during crack closure, which is an essential process in nonlinear photoacoustic/photothermal crack detection techniques. Our laser ultrasonics technique involves optical measurement of the transmission and mode conversion of the laser-generated surface acoustic waves caused by the crack. Reversible/irreversible modifications of asperities can be achieved via non-contact photothermal loading of the crack. Three photothermal loading cycles were realized in individual succession and were monitored using the laser ultrasonics technique at various experimental locations along the crack. In our experiments, each photothermal loading cycle includes multiple successive subcycles, in which the material is first heated and then cooled to its equilibrium temperature, thereby initiating local closing, followed by opening of the crack. Each subcycle is monitored twice using the laser ultrasonics technique, once each at the end of heating and cooling. Furthermore, each successive subcycle is accomplished at a higher power of heating laser than that of the previous subcycle. Significant differences in the peak-to-peak amplitude of the surface skimming longitudinal acoustic wave, which is excited by mode conversion of the Rayleigh wave by the crack, are revealed during the first cycle of photothermal loading. These differences clearly indicate a partial irreversibility of the mechanical processes occurring in the crack surfaces during subcycles of the first cycle. In a larger temporal scale, irreversible modification of crack surfaces is observed from the significant difference between the experimental results of the first photothermal loading cycle and the subsequent two cycles, whereas a reversible response of the crack surface to thermoelastic loading is observed from the similarity of the measurements accumulated during the two subsequent cycles.

A Survey On Road Crack Detection Techniques

2020 ◽  
Author(s):  
Sharmad Bhat ◽  
Saish Naik ◽  
Mandar Gaonkar ◽  
Pradnya Sawant ◽  
Shailendra Aswale ◽  
...  
Keyword(s):  
Crack Detection ◽  
Detection Techniques

Noise filtering techniques for Lamb waves in structural health monitoring

2018 ◽  
Vol 14 (4) ◽  
pp. 676-694
Author(s):  
Ambuj Sharma ◽  
Sandeep Kumar ◽  
Amit Tyagi
Keyword(s):  
Crack Detection ◽  
Lamb Waves ◽  
Random Noise ◽  
Matched Filtering ◽  
Noise Filtering ◽  
Content Type ◽  
Time Frequency ◽  
Structural Health ◽  
Filtering Technique ◽  
Filtering Techniques

Purpose The real challenges in online crack detection testing based on guided waves are random noise as well as narrow-band coherent noise; and to achieve efficient structural health assessment methodology, magnificent extraction of noise and analysis of the signals are essential. The purpose of this paper is to provide optimal noise filtering technique for Lamb waves in the diagnosis of structural singularities. Design/methodology/approach Filtration of time-frequency information of guided elastic waves through the noisy signal is investigated in the present analysis using matched filtering technique which “sniffs” the signal buried in noise and most favorable mother wavelet based denoising methods. The optimal wavelet function is selected using Shannon’s entropy criterion and verified by the analysis of root mean square error of the filtered signal. Findings Wavelet matched filter method, a newly developed filtering technique in this work and which is a combination of the wavelet transform and matched filtering method, significantly improves the accuracy of the filtered signal and identifies relatively small damage, especially in enormously noisy data. A comparative study is also performed using the statistical tool to know acceptability and practicability of filtered signals for guided wave application. Practical implications The proposed filtering techniques can be utilized in online monitoring of civil and mechanical structures. The algorithm of the method is easy to implement and found to be successful in accurately detecting damage. Originality/value Although many techniques have been developed over the past several years to suppress random noise in Lamb wave signal but filtration of interferences of wave modes and boundary reflection is not in a much matured stage and thus needs further investigation. The present study contains detailed information about various noise filtering methods, newly developed filtration technique and their efficacy in handling the above mentioned issues.

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