Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, we utilize an adaptive thresholding method to extract the cracks from road surface. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm.Comment: 6 pages, 8 figures, 2019 IEEE Intelligent Vehicles Symposiu

Improving road asset condition monitoring

Road networks often carry more than 80% of a country’s total passenger-km and over 50% of its freight ton-km according to the World Bank. Efficient maintenance of road networks is highly important. Road asset management, which is essential for maintenance programs, consist of monitoring, assessing and decision making necessary for maintenance, repair and/or replacement. This process is highly dependent on adequate and timely pavement condition data. Current practice for collecting and analysing such data is 99% manual. To optimize this process, research has been performed towards automation. Several methods to automatically detect road assets and pavement conditions are proposed. In this paper, we present an analysis of the current state of practice of road asset monitoring, a discussion of the limitations, and a qualitative evaluation of the proposed automation methods found in the literature. The objective of this paper is to understand the issues associated with current processes, and assess the available tools to address these problems. The current state of practice is categorized into: 1) type of data collected; 2) type of asset covered and 3) amount of information provided. The categories are evaluated in terms of a) accuracy; b) applicability (efficiency); c) cost; and d) overall improvement to current practice. Despite the methods available, the outcome of the study indicates that current condition monitoring lacks efficiency, and none provide a holistic solution to the problem of road asset condition monitorin

Automated Detection of Multiple Pavement Defects

Knowing the pavement condition is essential for efficiently deciding on maintenance programs. Current practice is predominantly manual with only 0.4% of inspections happening automatically. All methods in the literature aiming at automating condition assessment focus on two defects at most, or are too expensive for practical application. In this paper, the authors propose a low-cost method that automatically detects pavement defects simultaneously using parking camera video data. The types of defects addressed in this paper are two types of cracks (longitudinal and transverse), patches, and potholes. The method uses the semantic texton forests (STFs) algorithm as a supervised classifier on a calibrated region of interest (myROI), which is the area of the video frame depicting only the usable part of the pavement lane. It is validated using data collected from the local streets of Cambridge, U.K. Based on the results of multiple experiments, the overall accuracy of the method is above 82%, with a precision of more than 91% for longitudinal cracks, more than 81% for transverse cracks, more than 88% for patches, and more than 76% for potholes. The duration for training and classifying spans from 25 to 150 min, depending on the number of video frames used for each experiment. The contribution of this paper is dual: (1) an automated method for detecting several pavement defects at the same time, and (2) a method for calculating the region of interest within a video frame considering pavement manual guidelines.This material is based in part upon work supported by the National Science Foundation under Grant Number 1031329.This is the author accepted manuscript. The final version is available from the American Society of Civil Engineers via https://doi.org/10.1061/(ASCE)CP.1943-5487.000062

A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research

Pixel level pavement crack detection using deep convolutional neural network with residual blocks

Road condition monitoring, such as surface defects and pavement cracks detection, is an important task in road management. Automated road surface defect detection is also a challenging problem in computer vision and machine learning research due to the large variety of pavement crack structures, variable lighting conditions, interfering objects on the road surface such as trashes, fallen tree leaves and branches. In this work, we develop a deep learning-based method for automated road surface defect and pavement crack detection. We design a deep convolutional neural network based on using residual blocks to predict the heatmaps which indicate the location and intensity of defects and cracks. To reduce false detection rates, we couple this heatmap prediction network with a binary classification network which is able to determine if the input image patch is normal or has defects. We test our method on the CFD benchmark dataset. Experiment results show that the proposed network is very effective for pavement crack detection and has more advanced performance than other methods.by Yu HouIncludes bibliographical reference

Automated Detection and Characterization of Cracks on Concrete using Laser Scanning

Accurate crack detection and characterization on concrete are essential for the maintenance, safety, and serviceability of various infrastructures. In this paper, an innovative approach was developed to automatically measure the cracks from 3D point clouds collected by a phase-shift terrestrial laser scanner (TLS) (FARO Focus3D S120). The approach integrates several techniques to characterize the cracks, which include the deviation on point normal determined using k-nearest neighbor (kNN) and principal components analysis (PCA) algorithms to identify the cracks, and principal axes and curve skeletons of cracks to determine the projected and real dimensions of cracks, respectively. The coordinate transformation was then performed to estimate the projected dimensions of cracks. Curve skeletons and cross sections of cracks were extracted to represent the real dimensions. Two cases of surface cracks were used to validate the developed approach. Because of the differences in definitions of the crack dimension in the three methods and due to the curve shape of the crack, the width and depth of cracks obtained from the cross-section method and manual measurement were close but slightly smaller than those measured by the projection algorithm; whereas the length of cracks determined by the curve-skeletons method was slightly larger than those obtained by the manual measurement and projection method. The real dimension of a crack has good agreements with real situations when compared with the results of the manual measurement and projection method

New innovations in pavement materials and engineering: A review on pavement engineering research 2021

Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges. In the past 10 years, the pavement infrastructure strongly supports the rapid development of the global social economy. New theories, new methods, new technologies and new materials related to pavement engineering are emerging. Deterioration of pavement infrastructure is a typical multi-physics problem. Because of actual coupled behaviors of traffic and environmental conditions, predictions of pavement service life become more and more complicated and require a deep knowledge of pavement material analysis. In order to summarize the current and determine the future research of pavement engineering, Journal of Traffic and Transportation Engineering (English Edition) has launched a review paper on the topic of “New innovations in pavement materials and engineering: A review on pavement engineering research 2021”. Based on the joint-effort of 43 scholars from 24 well-known universities in highway engineering, this review paper systematically analyzes the research status and future development direction of 5 major fields of pavement engineering in the world. The content includes asphalt binder performance and modeling, mixture performance and modeling of pavement materials, multi-scale mechanics, green and sustainable pavement, and intelligent pavement. Overall, this review paper is able to provide references and insights for researchers and engineers in the field of pavement engineering

Identification of Top-down, Bottom-up, and Cement-Treated Reflective Cracks Using Convolutional Neural Network and Artificial Neural Network

The objective of this study was to formulate a Convolutional Neural Networks (CNN) model and to develop a decision-making tool using Artificial Neural Networks (ANN) to identify top-down, bottom-up, and cement treated (CT) reflective cracking in in-service flexible pavements. The CNN’s architecture consisted of five convolutional layers with three max-pooling layers and three fully connected layers. Input variables for the ANN model were pavement age, asphalt concrete (AC) thickness, annual average daily traffic (AADT), type of base, crack orientation, and crack location. The ANN network architecture consisted of an input layer of six neurons, a hidden layer of ten neurons, and a target layer of three neurons. The developed CNN model was found to achieve an accuracy of 93.8% and 91.0% in the testing and validation phases, respectively. The ANN based decision-making tool achieved an overall accuracy of 92% indicating its effectiveness in crack identification and classification. In the second phase of the study, the flexible pavement responses under a dual tire assembly were analyzed to identify the critical stress mechanisms for bottom-up and top-down cracking. Higher tensile strains were observed to occur underneath the tire ribs than away from them supporting the argument that both surface initiated and bottom-up fatigue cracking develop in or near the wheel paths. The incorporation of surface transverse tangential stresses increased the surface tensile strains near the tire ribs by approximately 68%, 63%, and 53% respectively for low, medium, and high volume flexible pavements indicating an increased potential for the initiation and development of top-down cracking when tangential stresses are considered. In contrast, this effect was observed to be minimal for the tensile strains at the bottom of the asphalt layer, which are the main pavement responses used in the prediction of fatigue cracking. Shrinkage cracking in cement treated base (CTB) was also modeled in finite element using displacement boundary conditions. The tensile stresses due to shrinkage strains in the cement treated base were observed to be comparable to the tensile strength of CTB at 7 days and higher at 56 days indicating the potential development of shrinkage cracks

DEEP LEARNING-BASED VISUAL CRACK DETECTION USING GOOGLE STREET VIEW IMAGES

DEEP LEARNING-BASED VISUAL CRACK DETECTION USING GOOGLE STREET VIEW IMAGE