Pavement Crack Detection Using High-Resolution 3D Line Laser Imaging Technology

Author(s):  
Yichang Tsai ◽  
Chenglong Jiang ◽  
Zhaohua Wang
2020 ◽  
Vol 13 (6) ◽  
pp. 1-9
Author(s):  
CHEN Xiao-Dong ◽  
◽  
AI Da-Hang ◽  
ZHANG Jia-Chen ◽  
CAI Huai-Yu ◽  
...  

2021 ◽  
Author(s):  
Nima Safaei ◽  
Omar Smadi ◽  
Babak Safaei ◽  
Arezoo Masoud

<p>Cracks considerably reduce the life span of pavement surfaces. Currently, there is a need for the development of robust automated distress evaluation systems that comprise a low-cost crack detection method for performing fast and cost-effective roadway health monitoring practices. Most of the current methods are costly and have labor-intensive learning processes, so they are not suitable for small local-level projects with limited resources or are only usable for specific pavement types.</p> <p>This paper proposes a new method that uses an improved version of the weighted neighborhood pixels segmentation algorithm to detect cracks in 2-D pavement images. This method uses the Gaussian cumulative density function as the adaptive threshold to overcome the drawback of fixed thresholds in noisy environments. The proposed algorithm was tested on 300 images containing a wide range of noise representative of different noise conditions. This method proved to be time and cost-efficient as it took less than 3.15 seconds per 320 × 480 pixels image for a Xeon (R) 3.70 GHz CPU processor to determine the detection results. This makes the model a perfect choice for county-level pavement maintenance projects requiring cost-effective pavement crack detection systems. The validation results were promising for the detection of low to severe-level cracks (Accuracy = 97.3%, Precision = 79.21%, Recall= 89.18% and F<sub>1</sub> score = 83.9%).</p>


2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Hongwei Lei ◽  
Jianlian Cheng ◽  
Qi Xu

This article introduces the application of image recognition technology in cement pavement crack detection and put forward to method for determining threshold about grayscale stretching. the algorithm is designed about binarization which has a self-adaptive characteristic. After the image is preprocessed, we apply 2D Wavelet and Laplace operator to process the image. According to the characteristic of pixel of gray image, an algorithm designed on binarization for Binary image. The feasibility of this method can be verified the image processed by comparing with the results of three algorithms: Otsu method, iteration method and fixed threshold method.


MRS Bulletin ◽  
1988 ◽  
Vol 13 (1) ◽  
pp. 13-18 ◽  
Author(s):  
J.H. Kinney ◽  
Q.C. Johnson ◽  
U. Bonse ◽  
M.C. Nichols ◽  
R.A. Saroyan ◽  
...  

Imaging is the cornerstone of materials characterization. Until the middle of the present century, visible light imaging provided much of the information about materials. Though visible light imaging still plays an extremely important role in characterization, relatively low spatial resolution and lack of chemical sensitivity and specificity limit its usefulness.The discovery of x-rays and electrons led to a major advance in imaging technology. X-ray diffraction and electron microscopy allowed us to characterize the atomic structure of materials. Many materials vital to our high technology economy and defense owe their existence to the understanding of materials structure brought about with these high-resolution methods.Electron microscopy is an essential tool for materials characterization. Unfortunately, electron imaging is always destructive due to the sample preparation that must be done prior to imaging. Furthermore, electron microscopy only provides information about the surface of a sample. Three dimensional information, of great interest in characterizing many new materials, can be obtained only by time consuming sectioning of an object.The development of intense synchrotron light sources in addition to the improvements in solid state imaging technology is revolutionizing materials characterization. High resolution x-ray imaging is a potentially valuable tool for materials characterization. The large depth of x-ray penetration, as well as the sensitivity of absorption crosssections to atomic chemistry, allows x-ray imaging to characterize the chemistry of internal structures in macroscopic objects with little sample preparation. X-ray imaging complements other imaging modalities, such as electron microscopy, in that it can be performed nondestructively on metals and insulators alike.


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