Infrastructure-Guided Connectivity-Enhanced Road Crack Detection and Estimation
Department of Mechanical and Aerospace Engineering（机械与航空航天工程系）；School of Engineering and Applied Science（工程与应用科学学院）
Haosong Xiao, Yamini Ramesh, Rishabh Shukla, Swarat Sarkar, Chaozhe R. He
备注：
Accepted and will be presented at the Fourth IEEE International Conference on Mobility: Operations, Services, and Technologies (MOST) on May 4 - 6, 2026 at Detroit, Michigan
In this paper, we report the world's first infrastructure-guided communication-enhanced road crack detection pipeline that is effective and implementable on passenger vehicles. We first design a customized communication protocol to transmit the region of interest from the infrastructure to the vehicle. With proper camera image processing (e.g., dynamic cropping and frame selection), the focused images are provided to the crack detection model. Leveraging state-of-the-art crack detection model backbones and a carefully prepared dataset comprising a forward-facing view with a crack, we train the model to improve crack-detection performance. We demonstrate the full detection pipeline on an experimental vehicle platform, showcase the detection effectiveness, and project future research directions.
With the advent of self-driving cars and autonomous robots, it is imperative to detect road impairments like cracks and potholes and to perform necessary evading maneuvers to ensure fluid journey for on-board passengers or equipment. We propose a fully autonomous robust real-time road crack and pothole detection algorithm which can be deployed on any GPU based conventional processing boards with an associated camera. The approach is based on a deep neural net architecture which detects cracks and potholes using texture and spatial features. We also propose pre-processing methods which ensure r…
University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）；University of International Business and Economics（对外经济贸易大学）
Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground-background imbalance. Fortunately, modification of loss function appears to solve this puzzle once and for all. In this paper, we propose a pixel-based adaptive weighted cross-entropy loss in conjunction with Jaccard distance to facilitate high-quality pixel-level road crack detection. Our work profoundly demonstrates the influence of loss functions on detection outcomes, and sheds light on the sophisticated consecutive improvements in the realm of crack detection. Specifically, t…
WP-CrackNet: A Collaborative Adversarial Learning Framework for End-to-End Weakly-Supervised Road Crack Detection
College of Electronic and Information Engineering, Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（电子信息工程学院，智能自主系统上海研究院，同济大学）；School of Internet of Things Engineering, Jiangnan University（物联网工程学院，江南大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电气电子工程学院，南洋理工大学）
Automated Road Crack Localization to Guide Highway Maintenance
HeiGIT at Heidelberg University（海德堡大学HeiGIT）；GIScience Research Group, Heidelberg University（海德堡大学地理信息科学研究组）；Interdisciplinary Centre of Scientific Computing (IWR), Heidelberg University（海德堡大学跨学科科学计算中心）；Lancaster University（兰卡斯特大学）
Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms
Frontiers Science Center for Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统前沿科学中心）；School of Computer Science and Technology, Donghua University（东华大学计算机科学与技术学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
Unity-Drive Technology Inc（深圳一清科技有限公司）；Hong Kong University of Science and Technology（香港科技大学）；National Engineering Research Center of Road Maintenance Technologies（道路养护技术国家工程研究中心）；University of Bristol（布里斯托大学）
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 …
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…
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…
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…
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…
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack Segmentation
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；SenseTime（商汤科技）
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Stanford University（斯坦福大学）
We propose a novel, connectivity-oriented loss function for training deep convolutional networks to reconstruct network-like structures, like roads and irrigation canals, from aerial images. The main idea behind our loss is to express the connectivity of roads, or canals, in terms of disconnections that they create between background regions of the image. In simple terms, a gap in the predicted road causes two background regions, that lie on the opposite sides of a ground truth road, to touch in prediction. Our loss function is designed to prevent such unwanted connections between background r…
SenseTime（商汤科技）；School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；School of Computer Science and Technology, Donghua University（东华大学计算机科学与技术学院）
Fine-tuning vision foundation model for crack segmentation in civil infrastructures
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Civil Engineering, Tsinghua University（清华大学土木工程系）；School of Civil and Resource Engineering, University of Science and Technology Beijing（北京科技大学土木与资源工程学院）
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 is a critical task that has the potential to drastically reduce labor-intensive building and road inspections currently being done manually. Recent studies in this field have significantly improved the detection accuracy. However, the methods often heavily rely on costly annotation processes. In addition, to handle a wide variety of target domains, new batches of annotations are usually required for each new environment. This makes the data annotation cost a significant bottleneck when deploying crack detection systems in real life. To resolve this issue, we formulate…
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…
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…
Pixel-level crack segmentation is widely studied due to its high impact on building and road inspections. While recent studies have made significant improvements in accuracy, they typically heavily depend on pixel-level crack annotations, which are time-consuming to obtain. In earlier work, we proposed to reduce the annotation cost bottleneck by reformulating the crack segmentation problem as a weakly-supervised problem -- i.e. the annotation process is expedited by sacrificing the annotation quality. The loss in annotation quality was remedied by refining the inference with per-pixel brightne…
National Technical University of Athens（雅典国立技术大学）；University of Macedonia（马其顿大学）
Recent studies indicate that deep learning plays a crucial role in the automated visual inspection of road infrastructures. However, current learning schemes are static, implying no dynamic adaptation to users' feedback. To address this drawback, we present a few-shot learning paradigm for the automated segmentation of road cracks, which is based on a U-Net architecture with recurrent residual and attention modules (R2AU-Net). The retraining strategy dynamically fine-tunes the weights of the U-Net as a few new rectified samples are being fed into the classifier. Extensive experiments show that…
Shantou University（汕头大学）；Sapienza University of Rome（罗马大学）；Pusan National University（釜山国立大学）
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 iden…
Advancements in artificial intelligence (AI) gives a great opportunity to develop an autonomous devices. The contribution of this work is an improved convolutional neural network (CNN) model and its implementation for the detection of road cracks, potholes, and yellow lane in the road. The purpose of yellow lane detection and tracking is to realize autonomous navigation of unmanned aerial vehicle (UAV) by following yellow lane while detecting and reporting the road cracks and potholes to the server through WIFI or 5G medium. The fabrication of own data set is a hectic and time-consuming task. …
Carnegie Mellon University（卡内基梅隆大学）；National University of Singapore（新加坡国立大学）；University of California Merced（加州大学默塞德分校）；Rutgers University（罗格斯大学）
The resolution of GPS measurements, especially in urban areas, is insufficient for identifying a vehicle's lane. In this work, we develop a deep LSTM neural network model LaNet that determines the lane vehicles are on by periodically classifying accelerometer samples collected by vehicles as they drive in real time. Our key finding is that even adjacent patches of road surfaces contain characteristics that are sufficiently unique to differentiate between lanes, i.e., roads inherently exhibit differing bumps, cracks, potholes, and surface unevenness. Cars can capture this road surface informati…
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…
Universitat Oberta de Catalunya（加泰罗尼亚开放大学）；ETH Zürich（苏黎世联邦理工学院）
This paper tackles the task of estimating the topology of road networks from aerial images. Building on top of a global model that performs a dense semantical classification of the pixels of the image, we design a Convolutional Neural Network (CNN) that predicts the local connectivity among the central pixel of an input patch and its border points. By iterating this local connectivity we sweep the whole image and infer the global topology of the road network, inspired by a human delineating a complex network with the tip of their finger. We perform an extensive and comprehensive qualitative an…
The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong Polytechnic University（香港理工大学）
Road curb detection is important for autonomous driving. It can be used to determine road boundaries to constrain vehicles on roads, so that potential accidents could be avoided. Most of the current methods detect road curbs online using vehicle-mounted sensors, such as cameras or 3-D Lidars. However, these methods usually suffer from severe occlusion issues. Especially in highly-dynamic traffic environments, most of the field of view is occupied by dynamic objects. To alleviate this issue, we detect road curbs offline using high-resolution aerial images in this paper. Moreover, the detected r…
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…
The University of Tokyo（东京大学）；Urban-X Technologies, Inc.（Urban-X科技公司）
Road rutting is a severe road distress that can cause premature failure of road incurring early and costly maintenance costs. Research on road damage detection using image processing techniques and deep learning are being actively conducted in the past few years. However, these researches are mostly focused on detection of cracks, potholes, and their variants. Very few research has been done on the detection of road rutting. This paper proposes a novel road rutting dataset comprising of 949 images and provides both object level and pixel level annotations. Object detection models and semantic …
Fraunhofer Institut für Techno- und Wirtschaftsmathematik（弗劳恩霍夫工业数学研究所）；Technische Universität Kaiserslautern（凯泽斯劳滕工业大学）
Concrete is the standard construction material for buildings, bridges, and roads. As safety plays a central role in the design, monitoring, and maintenance of such constructions, it is important to understand the cracking behavior of concrete. Computed tomography captures the microstructure of building materials and allows to study crack initiation and propagation. Manual segmentation of crack surfaces in large 3d images is not feasible. In this paper, automatic crack segmentation methods for 3d images are reviewed and compared. Classical image processing methods (edge detection filters, templ…
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）
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 …
StripRFNet: A Strip Receptive Field and Shape-Aware Network for Road Damage Detection
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院数字地球科学重点实验室）；International Research Center of Big Data for Sustainable Development Goals（可持续发展大数据国际研究中心）；University of Chinese Academy of Sciences（中国科学院大学）；Southeast University（东南大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；Qatar Computing Research Institute, HBKU（卡塔尔计算研究所，哈马德·本·哈利法大学）
Mapping road networks is currently both expensive and labor-intensive. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity. We show that these segmentation methods have high error rates because noisy CNN outputs are difficult to correct. We propose RoadTracer, a new method to automatically construct accurate road network maps from aerial images. RoadTracer uses an i…
FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM
School of Electronic and Electronics Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；Leiden University（莱顿大学）；School of Information Technology, Murdoch University（默多克大学信息技术学院）；University of Washington（华盛顿大学）；Leiden Institute of Advanced Computer Science, Leiden University（莱顿大学高级计算机科学研究所）
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）
TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Alipurduar Government Engineering and Management College（阿里布尔杜阿尔政府工程与管理学院）；MBZUAI（Mohamed bin Zayed 人工智能大学）；Carnegie Mellon University（卡内基梅隆大学）
Prefectural University of Hiroshima（广岛县立大学）；Mitsui Consultants Co. Ltd.（三井咨询有限公司）
We have developed an adaptive structural Deep Belief Network (Adaptive DBN) that finds an optimal network structure in a self-organizing manner during learning. The Adaptive DBN is the hierarchical architecture where each layer employs Adaptive Restricted Boltzmann Machine (Adaptive RBM). The Adaptive RBM can find the appropriate number of hidden neurons during learning. The proposed method was applied to a concrete image benchmark data set SDNET2018 for crack detection. The dataset contains about 56,000 crack images for three types of concrete structures: bridge decks, walls, and paved roads.…
Classifying and counting vehicles in road traffic has numerous applications in the transportation engineering domain. However, the wide variety of vehicles (two-wheelers, three-wheelers, cars, buses, trucks etc.) plying on roads of developing regions without any lane discipline, makes vehicle classification and counting a hard problem to automate. In this paper, we use state of the art Convolutional Neural Network (CNN) based object detection models and train them for multiple vehicle classes using data from Delhi roads. We get upto 75% MAP on an 80-20 train-test split using 5562 video frames …
