Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.
Crack detection plays a key role in automated pavement inspection. Although a large number of algorithms have been developed in recent years to further boost performance, there are still remaining challenges in practice, due to the complexity of pavement images. To further accelerate the development and identify the remaining challenges, this paper conducts a comparison study to evaluate the performance of the state of the art crack detection algorithms quantitatively and objectively. A more comprehensive annotated pavement crack dataset (NHA12D) that contains images with different viewpoints …
Shantou University（汕头大学）；South China Agricultural University（华南农业大学）；Oklahoma State University（俄克拉荷马州立大学）
The acquisition and evaluation of pavement surface data play an essential role in pavement condition evaluation. In this paper, an efficient and effective end-to-end network for automatic pavement crack segmentation, called RHA-Net, is proposed to improve the pavement crack segmentation accuracy. The RHA-Net is built by integrating residual blocks (ResBlocks) and hybrid attention blocks into the encoder-decoder architecture. The ResBlocks are used to improve the ability of RHA-Net to extract high-level abstract features. The hybrid attention blocks are designed to fuse both low-level features …
Georgia Institute of Technology（佐治亚理工学院）；Twentieth Century Fox（二十世纪福克斯公司）；Starfleet Academy（星际舰队学院）；Tyrell Inc.（泰伦公司）
Fully convolutional network is a powerful tool for per-pixel semantic segmentation/detection. However, it is problematic when coping with crack detection using partially accurate ground truths (GTs): the network may easily converge to the status that treats all the pixels as background (BG) and still achieves a very good loss, named "All Black" phenomenon, due to the unavailability of accurate GTs and the data imbalance. To tackle this problem, we propose crack-patch-only (CPO) supervised generative adversarial learning for end-to-end training, which forces the network to always produce crack-…
University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Amazon（亚马逊公司）
Pavement conditions are a critical aspect of asset management and directly affect safety. This study introduces a deep neural network method called U-Net for pavement crack segmentation based on drone-captured images to reduce the cost and time needed for airport runway inspection. The proposed approach can also be used for highway pavement conditions assessment during off-peak periods when there are few vehicles on the road. In this study, runway pavement images are collected using drone at various heights from the Fitchburg Municipal Airport (FMA) in Massachusetts to evaluate their quality a…
Shantou University（汕头大学）；Sapienza University of Rome（罗马大学）；Pusan National University（釜山国立大学）
Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the mult…
Chongqing University（重庆大学）；Chongqing City Management College（重庆城市管理职业学院）；Chongqing University of Technology（重庆理工大学）；China Merchants Road Information Technology (Chongqing) Co., Ltd（招商局公路信息技术（重庆）有限公司）
Deep learning-based pavement cracks detection methods often require large-scale labels with detailed crack location information to learn accurate predictions. In practice, however, crack locations are very difficult to be manually annotated due to various visual patterns of pavement crack. In this paper, we propose a Deep Domain Adaptation-based Crack Detection Network (DDACDN), which learns domain invariant features by taking advantage of the source domain knowledge to predict the multi-category crack location information in the target domain, where only image-level labels are available. Spec…
Automated pavement crack detection is a challenging task that has been researched for decades due to the complicated pavement conditions in real world. In this paper, a supervised method based on deep learning is proposed, which has the capability of dealing with different pavement conditions. Specifically, a convolutional neural network (CNN) is used to learn the structure of the cracks from raw images, without any preprocessing. Small patches are extracted from crack images as inputs to generate a large training database, a CNN is trained and crack detection is modeled as a multi-label class…
Temple University（天普大学）；University of Pittsburgh（匹兹堡大学）；Toyota Research Institute（丰田研究院）；TuSimple（图森未来）
Pavement crack detection is a critical task for insuring road safety. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this progress. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavements and possible shadows with similar intensity. Inspired by recent advances of deep learning in computer vision, we propose a novel network architecture, named Feature Pyramid and Hierarchical Boosting Network (FPHBN), for pa…
Jiangsu JARI Technology Group Shanghai Branch（江苏杰瑞科技集团上海分公司）
Compared to NDT and health monitoring method for cracks in engineering structures, surface crack detection or identification based on visible light images is non-contact, with the advantages of fast speed, low cost and high precision. Firstly, typical pavement (concrete also) crack public data sets were collected, and the characteristics of sample images as well as the random variable factors, including environmental, noise and interference etc., were summarized. Subsequently, the advantages and disadvantages of three main crack identification methods (i.e., hand-crafted feature engineering, m…
Enhancing Pavement Crack Classification with Bidirectional Cascaded Neural Networks
Al-Ahliyya Amman University（阿曼阿赫利耶大学）；Ministry of Public Work of Kuwait（科威特公共工程部）；Queensland University of Technology（昆士兰科技大学）；Arab American University（阿拉伯美国大学）
Wuhan University（武汉大学）；University of Waterloo（滑铁卢大学）
Timely, accurate and automatic detection of pavement cracks is necessary for making cost-effective decisions concerning road maintenance. Conventional crack detection algorithms focus on the design of single or multiple crack features and classifiers. However, complicated topological structures, varying degrees of damage and oil stains make the design of crack features difficult. In addition, the contextual information around a crack is not investigated extensively in the design process. Accordingly, these design features have limited discriminative adaptability and cannot fuse effectively wit…
Automated pavement crack image segmentation is challenging because of inherent irregular patterns, lighting conditions, and noise in images. Conventional approaches require a substantial amount of feature engineering to differentiate crack regions from non-affected regions. In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images. Our approach requires minimal feature engineering compared to other machine learning techniques. We propose a U-Net-based network architecture in which we replace the encoder wi…
Fast Measuring Pavement Crack Width by Cascading Principal Component Analysis
Faculty of Information Technology, Beijing University of Technology, No.100 Pingleyuan Road, Chaoyang District, Beijing 100124, China（信息技术学院，北京理工大学，北京市朝阳区平乐园路100号，北京100124，中国）
Compared with contact detection techniques, pavement crack identification with visual images via deep learning algorithms has the advantages of not being limited by the material of object to be detected, fast speed and low cost. The fundamental frameworks and typical model architectures of transfer learning (TL), encoder-decoder (ED), generative adversarial networks (GAN), and their common modules were first reviewed, and then the evolution of convolutional neural network (CNN) backbone models and GAN models were summarized. The crack classification, segmentation performance, and effect were t…
Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Advanced Institute of Information Technology, Peking University（北京大学前沿信息研究院）；University of International Business and Economics（对外经济贸易大学）；Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences（中国科学院虚拟经济与数据科学研究中心）
Pixel-level road crack detection has always been a challenging task in intelligent transportation systems. Due to the external environments, such as weather, light, and other factors, pavement cracks often present low contrast, poor continuity, and different sizes in length and width. However, most of the existing studies pay less attention to crack data under different situations. Meanwhile, recent algorithms based on deep convolutional neural networks (DCNNs) have promoted the development of cutting-edge models for crack detection. Nevertheless, they usually focus on complex models for good …
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Regular pavement inspection plays a significant role in road maintenance for safety assurance. Existing methods mainly address the tasks of crack detection and segmentation that are only tailored for long-thin crack disease. However, there are many other types of diseases with a wider variety of sizes and patterns that are also essential to segment in practice, bringing more challenges towards fine-grained pavement inspection. In this paper, our goal is not only to automatically segment cracks, but also to segment other complex pavement diseases as well as typical landmarks (markings, runway l…
Istituto di Matematica Applicata e Tecnologie Informatiche ‘E. Magenes’ - CNR（意大利国家研究委员会“E. 马涅斯”应用数学与信息技术研究所）；University of Chile（智利大学）；University of Science, Ho Chi Minh City（胡志明市科学大学）；Viet Nam National University, Ho Chi Minh City（胡志明市越南国家大学）；John von Neumann Institute（约翰·冯·诺依曼研究所）
This paper describes the methods submitted for evaluation to the SHREC 2022 track on pothole and crack detection in the road pavement. A total of 7 different runs for the semantic segmentation of the road surface are compared, 6 from the participants plus a baseline method. All methods exploit Deep Learning techniques and their performance is tested using the same environment (i.e.: a single Jupyter notebook). A training set, composed of 3836 semantic segmentation image/mask pairs and 797 RGB-D video clips collected with the latest depth cameras was made available to the participants. The meth…
Automatic crack detection on pavement surfaces is an important research field in the scope of developing an intelligent transportation infrastructure system. In this paper, a cost effective solution for road crack inspection by mounting commercial grade sport camera, GoPro, on the rear of the moving vehicle is introduced. Also, a novel method called ConnCrack combining conditional Wasserstein generative adversarial network and connectivity maps is proposed for road crack detection. In this method, a 121-layer densely connected neural network with deconvolution layers for multi-level feature fu…
Curtin University（科廷大学）；Gwangju Institute of Science and Technology (GIST)（光州科学技术院）；RMIT University（皇家墨尔本理工大学）
In the past few years, the performance of road defect detection has been remarkably improved thanks to advancements on various studies on computer vision and deep learning. Although a large-scale and well-annotated datasets enhance the performance of detecting road pavement defects to some extent, it is still challengeable to derive a model which can perform reliably for various road conditions in practice, because it is intractable to construct a dataset considering diverse road conditions and defect patterns. To end this, we propose an unsupervised approach to detecting road defects, using A…
Chongqing University（重庆大学）；Chongqing Jiaotong University（重庆交通大学）；San Diego State University（圣地亚哥州立大学）
We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and Effic…
EfficientCrackNet: A Lightweight Model for Crack Segmentation
Aligarh Muslim University（阿里格尔穆斯林大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Chengdu University（成都大学）；Monash University Malaysia（莫纳什大学马来西亚分校）；Tohoku University（东北大学）
BeltCrack: the First Sequential-image Industrial Conveyor Belt Crack Detection Dataset and Its Baseline with Triple-domain Feature Learning
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Suzhou Red Shield Intelligent Manufacturing Co., Ltd.（苏州红盾智能制造有限公司）
Lamar University（拉马尔大学）；Stanley Black & Decker（史丹利百得）
Pavement condition evaluation is essential to time the preventative or rehabilitative actions and control distress propagation. Failing to conduct timely evaluations can lead to severe structural and financial loss of the infrastructure and complete reconstructions. Automated computer-aided surveying measures can provide a database of road damage patterns and their locations. This database can be utilized for timely road repairs to gain the minimum cost of maintenance and the asphalt's maximum durability. This paper introduces a deep learning-based surveying scheme to analyze the image-based d…
Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Rizzo International, Inc.（里佐国际公司）
The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is time-consuming and subjective to the inspectors. Several researchers have tried tackling this problem using traditional Image Processing or learning-based techniques. However, their scope of work is limited to detecting cracks on a single type of surface (walls, pavements, glass, etc.). The metrics used to evaluate these methods are also varied across the literature, making it challenging to compare techniques. This paper addresses these problems by…
StructDamage:A Large Scale Unified Crack and Surface Defect Dataset for Robust Structural Damage Detection
Rhineland-Palatinate Technical University of Kaiserslautern-Landau（凯撒斯劳滕-兰道技术大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；Intelligentx GmbH；BiogentX；Department of Core Informatics, Graduate School of Informatics ,Osaka Metropolitan University（大阪 Metropolitan 大学信息学研究生院核心信息学系）
University of Missouri Columbia（密苏里大学哥伦比亚分校）；WSP USA（美国WSP公司）
Automatic detection and classification of pavement distresses is critical in timely maintaining and rehabilitating pavement surfaces. With the evolution of deep learning and high performance computing, the feasibility of vision-based pavement defect assessments has significantly improved. In this study, the authors deploy state-of-the-art deep learning algorithms based on different network backbones to detect and characterize pavement distresses. The influence of different backbone models such as CSPDarknet53, Hourglass-104 and EfficientNet were studied to evaluate their classification perform…
Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen（格罗宁根大学约翰·伯努利数学与计算机科学研究所）；Intelligent Computer Systems, University of Malta（马耳他大学智能计算机系统研究所）
The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. %The visual system of the brain has remarkable abilities to detect curvilinear structures in noisy images. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired…
University of Technology Sydney（悉尼科技大学）；UTS - VNU Joint Technology and Innovation Research Centre（悉尼科技大学-越南国立大学联合技术创新研究中心）；University of Engineering and Technology, Vietnam National University, Hanoi（河内越南国立大学工程技术大学）
Drone imagery is increasingly used in automated inspection for infrastructure surface defects, especially in hazardous or unreachable environments. In machine vision, the key to crack detection rests with robust and accurate algorithms for image processing. To this end, this paper proposes a deep learning approach using hierarchical convolutional neural networks with feature preservation (HCNNFP) and an intercontrast iterative thresholding algorithm for image binarization. First, a set of branch networks is proposed, wherein the output of previous convolutional blocks is half-sizedly concatena…
PEDESTRIAN: An Egocentric Vision Dataset for Obstacle Detection on Pavements
CYENS Centre of Excellence（CYENS卓越中心）；Open University Cyprus（塞浦路斯开放大学）；Department of Communication and Internet Studies（通信与互联网研究系）；Cyprus University of Technology（塞浦路斯技术大学）；AI Cyprus Ethical Novelties Ltd（塞浦路斯AI伦理创新有限公司）；Department of Multimedia and Graphic Arts（多媒体与图形艺术系）
Northwestern Polytechnical University（西北工业大学）；Shaanxi Normal University（陕西师范大学）
A reasonable evaluation standard underlies construction of effective deep learning models. However, we find in experiments that the automatic crack detectors based on deep learning are obviously underestimated by the widely used mean Average Precision (mAP) standard. This paper presents a study on the evaluation standard. It is clarified that the random fractal of crack disables the mAP standard, because the strict box matching in mAP calculation is unreasonable for the fractal feature. As a solution, a fractal-available evaluation standard named CovEval is proposed to correct the underestimat…
Joint raveled or spalled damage (henceforth called joint damage) can affect the safety and long-term performance of concrete pavements. It is important to assess and quantify the joint damage over time to assist in building action plans for maintenance, predicting maintenance costs, and maximize the concrete pavement service life. A framework for the accurate, autonomous, and rapid quantification of joint damage with a low-cost camera is proposed using a computer vision technique with a deep learning (DL) algorithm. The DL model is employed to train 263 images of sawcuts with joint damage. The…
From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring
Intelligent Maintenance and Operations Systems (IMOS), EPFL（智能维护与运营系统研究所（IMOS），洛桑联邦理工学院）；Environmental Computational Science and Earth Observation (ECEO), EPFL（环境计算科学与地球观测研究所（ECEO），洛桑联邦理工学院）
Robust Mask R-CNN (Mask Regional Convolu-tional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earth-quakes. We curated a new dataset with 2,021 labeled images for training and validation and aimed to find end-to-end deep neural networks for crack detection in the field. With data augmentation and parameters fine-tuning, Path Aggregation Network (PANet) with spatial attention mechanisms and High-resolution Network (HRNet) are introduced into Mask R-CNNs. The tests on three pu…
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）；The University of Tokyo（东京大学）；UrbanX Technologies, Inc.（UrbanX科技公司）
The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The images have been annotated with more than 55,000 instances of road damage. Four types of road damage, namely longitudinal cracks, transverse cracks, alligator cracks, and potholes, are captured in the dataset. The annotated dataset is envisioned for developing deep learning-based methods to detect and classify road damage automatically. The dataset has been released as a part of the Crowd sensing-based Ro…
Bridges are an essential part of the transportation infrastructure and need to be monitored periodically. Visual inspections by dedicated teams have been one of the primary tools in structural health monitoring (SHM) of bridge structures. However, such conventional methods have certain shortcomings. Manual inspections may be challenging in harsh environments and are commonly biased in nature. In the last decade, camera-equipped unmanned aerial vehicles (UAVs) have been widely used for visual inspections; however, the task of automatically extracting useful information from raw images is still …
