A potential crack region method to detect crack using image processing of multiple thresholding

In this paper, a potential crack region method is proposed to detect road pavement cracks by using the adaptive threshold. To reduce the noises of the image, the pre-treatment algorithm was applied according to the following steps: grayscale processing, histogram equalization, filtering traffic lane. From the image segmentation methods, the algorithm combines the global threshold and the local threshold to segment the image. According to the grayscale distribution characteristics of the crack image, the sliding window is used to obtain the window deviation, and then, the deviation image is segmented based on the maximum inter-class deviation. Obtain a potential crack region and then perform a local threshold-based segmentation algorithm. Real images of pavement surface were used at the Su Tong Li road in Suzhou, China. It was found that the proposed approach could give a more explicit description of pavement cracks in images. The method was tested on 509 images of the German asphalt pavement distress (Gap) dataset: The test results were found to be promising (precision = 0.82, recall = 0.81, F1 score = 0.83).

Methodology for Interactive Labeling of Patched Asphalt Pavement Images Based on U-Net Convolutional Neural Network

Image recognition based on deep learning generally demands a huge sample size for training, for which the image labeling becomes inevitably laborious and time-consuming. In the case of evaluating the pavement quality condition, many pavement distress patching images would need manual screening and labeling, meanwhile the subjectivity of the labeling personnel would greatly affect the accuracy of image labeling. In this study, in order for an accurate and efficient recognition of the pavement patching images, an interactive labeling method is proposed based on the U-Net convolutional neural network, using active learning combined with reverse and correction labeling. According to the calculation results in this paper, the sample size required by the interactive labeling is about half of the traditional labeling method for the same recognition precision. Meanwhile, the accuracy of interactive labeling method based on the mean intersection over union (mean_IOU) index is 6% higher than that of the traditional method using the same sample size and training epochs. In addition, the accuracy analysis of the noise and boundary of the prediction results shows that this method eliminates 92% of the noise in the predictions (the proportion of noise is reduced from 13.85% to 1.06%), and the image definition is improved by 14.1% in terms of the boundary gray area ratio. The interactive labeling is considered as a significantly valuable approach, as it reduces the sample size in each epoch of active learning, greatly alleviates the demand for manpower, and improves learning efficiency and accuracy.

Adaptation and Calibration of the Faulting Model for Thin Concrete Pavements

Joint faulting is a pavement distress that affects the comfort level of jointed plain concrete pavements. The appearance of joint faulting usually occurs in areas of high traffic of trucks at high speed. Variables such as level of rainfall and the erodibility of the subbase increases the magnitude of this phenomenon. To predict joint faulting in Thin Concrete Pavements, the design software OptiPave2, launched in 2012, used the same model developed for the Mechanistic Empirical Pavement Design Guide (MEPDG), which uses an energy differential model. After 6 years of the release of the software and after 10 years since the construction of some thin concrete pavement projects, there are pavements with clear signs of joint faulting and others without. For this reason, the OptiPave2 model was reviewed and compared with field data, concluding that the faulting model needed to be adjusted This new model was calibrated with the data from existing concrete pavement projects.

Improvement of Pavement Subgrade by Adding Cement and Fly Ash to Natural Desert Sand

Soil characteristics are paramount to design pavements and to assess the economic viability of a road. In the desert, such as that found in southern Libya, the very poor quality of soils leads to important pavement distress such as cracks, rutting, potholes, and lateral shear failure on the edges. To improve the strength of desert sand, an innovative approach is proposed, consisting of adding manufactured sand, ordinary Portland cement (OPC), and fly ash (FA) as a binder. OPC and FA improve the characteristics of mixes of crushed fine aggregate (CFA) and natural desert sand (NDS). These results are based on a gradation of two sand sources to determine the particle distribution and X-ray fluorescence (XRF) to determine their chemical and physical properties, respectively. This research assesses the effect of cement and fly ash on the geotechnical behavior of two mixtures of fine desert and manufactured sands (30:70% and 50:50%). The mix composed of 26% of CFA, 62% of NDS, 5% of OPC, and 7% of FA shows optimal results in terms of strength, compaction, and bearing capacity characteristics.

Automated Pavement Distress Detection Using Image Processing Techniques

Pavement crack and pothole identification are important tasks in transportation maintenance and road safety. This study offers a novel technique for automatic asphalt pavement crack and pothole detection which is based on image processing. Different types of cracks (transverse, longitudinal, alligator-type, and potholes) can be identified with such techniques. The goal of this research is to evaluate road surface damage by extracting cracks and potholes, categorizing them from images and videos, and comparing the manual and the automated methods. The proposed method was tested on 50 images. The results obtained from image processing showed that the proposed method can detect cracks and potholes and identify their severity levels with a medium validity of 76%. There are two kinds of methods, manual and automated, for distress evaluation that are used to assess pavement condition. A committee of three expert engineers in the maintenance department of the Mayoralty of Baghdad did the manual assessment of a highway in Baghdad city by using a Pavement Condition Index (PCI). The automated method was assessed by processing the videos of the road. By comparing the automated with the manual method, the accuracy percentage for this case study was 88.44%. The suggested method proved to be an encouraging solution for identifying cracks and potholes in asphalt pavements and sorting their severity. This technique can replace manual road damage assessment.

Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images

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.