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Societal economic vitality and growth is intimately tied to the state of infrastructure and its ability to safely and efficiently handle the transfer of goods from one point to another. Pavement management, preservation, and rehabilitation strategies are critical components in maintaining the viability of infrastructure and economy over the long term. Roadway networks contain millions of miles of pavements, and maintenance operations of these systems cost upwards of $25 billion per year [1]. As part of the maintenance operations, pavement surveys, which include both surface and subsurface assessments, are required frequently to assess the state of the pavement and help to prioritize rehabilitative and preservative action. Moreover, according to the National Highway Traffic Safety Administration, 16% of traffic crashes are produced due to roadway environmental factors mainly by poor pavement conditions [2]. Poor road conditions also lead to excessive wear on vehicles and tend to increase the number of delays and crashes which can lead to additional financial losses [3]. Currently, manual inspection is the most common technique for identifying pavement distress road surveys [4]. Manual inspection can be, however, time-consuming, costly, and labor-intensive. Furthermore, during manual inspection operations, human visual error is possible, the operation itself can be unsafe due to the passing of nearby motor vehicles, and the operations may impede traffic flow [5]. To overcome the limitations of manual inspection, automated and/or semiautomated crack detection techniques can be developed to measure, monitor, and map the evolution of the pavement surface and subsurface structure and distress profile [6]. Semiautomated modern pavement distress mapping or diagnosis techniques need to be nondestructive, cost-effective, accurate, enabling data acquisition at high-speed, and relatively user and environmentally friendly [7]. As part of an effort to lower costs and accelerate maintenance operations, transportation departments are prioritizing the development of automated systems profiling systems for pavement distress assessment [8]. There remains a need, however, to develop automated and real-time distress mapping and assessment tools that can provide the end-user with large quantities of information related to the distress type, geometry, and distress source without manual surveillance either in situ or by proxy. The prominent solution of replacing expert inspectors with robots that can automatically gather data and analyze them has been studied or suggested extensively in recent years.




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Despite all benefits of automated data collection methods, it leads to vast amount of raw pavement data. Interpreting the raw data needs human expert for analysis and decision making. Regarding the importance of on time maintenance of pavements, it is impossible to process all raw data relying on expert human performance which has led the researchers to develop automatic intelligent algorithms for processing gathered raw data. The utilization of computer vision methods for pavement engineering applications has grown exponentially over the last few decades [11], while many challenges should be addressed to achieve a full and seamless realization due to unwanted and highly variable image noises from random variation of brightness color, camera, and the environment [12]. In recent years, many transportation and highway agencies in the US have become interested in image processing-based methods for analyzing collected raw data from highways and roads [13].


The integration of computer vision methods using deep convolutional neural networks (CNNs) shows exceptional promise for use in crack detection applications, but require many images for the training process [10, 27, 33]. Although utilizing CNNs improves the crack detection accuracy, there are known drawbacks in the conventional approaches for studying the cracks [10]. In the available works [10, 27], whether asphalt pavement or other surfaces, the objective is to distinguish cracked areas of pavement from uncracked ones that yields to a binary decision with two outcomes: cracked or noncracked. Due to the proven outstanding performance of deep learning in contrast with other machine learning methods like SVM, Adaboost, and random forest, still some shortage exists. For instance, in [11], although it is proved that a CNN has the capability of detecting cracks with high accuracy, authors suggest an optimization for the CNN for future improvements. Also, using transfer learning for pavement distress detection, Gopalakrishnan et al. [29] suggested to add a feature for evaluating severity of detected cracks, which shows possible further improvements for pavement distress detection. In deep learning-based methods, for pavement distress detection, the current focus is on improving the accuracy of neural network for identifying cracks. Also, most of the recent work in this application uses AlexNet, VGG-16 CNN architecture, and some transfer learning methods for pavement distress detection task. It is important to consider that most of the mentioned architectures are designed and tested on datasets that do not include pavement distress data. Although in many cases transfer learning is applicable for reducing training time and improving accuracy, the objects in datasets like MNIST, ImageNet, CIFAR-100, etc. do not share similar patterns in pavement applications, so using transfer learning with similar CNN architectures is limited.


As reviewed above, deep learning-based methods for pavement distress detection improves false detection accuracy within noncracked pavements, whether using different CNN architectures or transfer learning or pretrained models. This paper proposes an approach to geometrically map a surface crack on asphalt pavement using a technique that involves image partitioning and crack geometrical and spatial classification. This technique allows the user to both detect the presence and map a crack on the road surface in real time using raw input images. The work extends the functionality of CNN-based classification techniques, which up to date are limited to only crack presence detection and do not provide simultaneous geometrical mapping of the object [34, 35]. Crack images are aggregated in the database and indexed according to their orientation and spatial position within a squared partitioned area of the larger raw image file, which then allows the position and orientation to be estimated heuristically using thirteen unique categories. By applying this approach, instead of predicting crack position in each frame by a marginal error that depends on the searching window, we not only are able to detect and classify the cracks, but also map the crack and avoid errors caused by the searching window.


The primary objective of this work is to use real-time images to map cracks on the surface of an asphalt pavement. A crack is defined as a mechanical or thermal strain-induced separation of material. This material separation allows moisture to infiltrate the pavement structure internally, leading to premature failure or accelerated deterioration. Cracks are classified by their geometric orientation, source, width, and concentration per unit length or area. Only cracks visible and distinguishable to the naked eye are considered in the distress survey.


In this section, the test results of the trained CNN are presented in two subsections: (i) the performance of the algorithm was tested on several single images with various crack shapes and (ii) a real-time mapping evaluation was done on a captured video that is obtained from a randomly selected pavement roadway. The test images from the training and testing database were not used, not filtered or modified, and taken under varying light condition and camera position. Also, in this paper, MATLAB was used for training and optimizing the CNN and implementing the CMA algorithm.


The proposed algorithm in this work improves upon existing work [10] by integrating crack detection with a crack mapping using image segmentation and classification within a CNN architecture. In addition, the optimized CNN architecture proposed here uses a significantly lower number of filters in the convolution layer (256) leading to reduced computational demand in both CNN training and real-time processing. As mentioned in Section 6, the BOA is used to compute the HPs. To verify the fact that the selected optimal values maximize the CNN accuracy, during the training process, all HPs were perturbed by a , , , and white noise. As shown in Table 1, perturbing the HPs by decreases the accuracy by about , while as it increases to , the accuracy decreases by at most 8%.


In this paper, an algorithm for mapping road cracks in real time using convolutional neural networks was proposed and tested. Authors gathered the database for this work, and due to limited available resources, the size of the database was limited to 6695 images. The convolutional neural network in this work was optimized using the Bayesian optimization algorithm. A heuristic algorithm for real-time crack mapping was introduced and tested on different images with complicated crack position and orientation. Also, a video was recorded and processed for testing the real-time ability of the algorithm. Although the database was carefully selected and curated in this work, the authors attempted to include a robust population of crack images to improve the selection and classification power of the CNN. However, this study is limited to only one block size and 13 classification categories. Certainly, for commercial applications, increasing the number of images within the training and increasing the computing power will allow users to reduce the size of the tiles and increase the number of classification categories which may further refine the smoothness of the mapping segments. The mapping results via the CMA may also be used for crack type classification and causation, analyzing what type of asphalt is more prone to cracking, what type of asphalts are more suitable for different road conditions with respect to the traffic, and how to choose the best asphalt for various conditions, and finally estimating the repair and protection costs for each individual road type. Analyzing the crack propagation patterns based on geographical information of the road using the CMA provides more analytical information in combination with other data that could help during the decision making process for road construction. 350c69d7ab


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