Monitoring the technical condition of infrastructure is a crucial element to its maintenance. Currently applied methods are outdated, labour-intensive and inaccurate. At the same time, the latest methods using Artificial Intelligence techniques are severely limited in their application due to two main factors -- labour-intensive gathering of new datasets and high demand for computing power. We propose to utilize custom made framework -- KrakN, to overcome these limiting factors. It enables the development of unique infrastructure defects detectors on digital images, achieving the accuracy of above 90%. The framework supports semi-automatic creation of new datasets and has modest computing power requirements. It is implemented in the form of a ready-to-use software package openly distributed to the public. Thus, it can be used to immediately implement the methods proposed in this paper in the process of infrastructure management by government units, regardless of their financial capabilities.
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）
Surface cracks are a very common indicator of potential structural faults. Their early detection and monitoring is an important factor in structural health monitoring. Left untreated, they can grow in size over time and require expensive repairs or maintenance. With recent advances in computer vision and deep learning algorithms, the automatic detection and segmentation of cracks for this monitoring process have become a major topic of interest. This review aims to give researchers an overview of the published work within the field of crack analysis algorithms that make use of deep learning. I…
Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces
Maseeh Department of Civil Architectural and Environmental Engineering（梅塞德斯土木建筑与环境工程系）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Ingram School of Engineering（英格拉姆工程学院）；Texas State University（德克萨斯州立大学）；Department of Civil and Environmental Engineering（土木与环境工程系）
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…
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）
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）
National Institute of Technology Durgapur（杜尔加普尔国家理工学院）
Due to cyclic loading and fatigue stress cracks are generated, which affect the safety of any civil infrastructure. Nowadays machine vision is being used to assist us for appropriate maintenance, monitoring and inspection of concrete structures by partial replacement of human-conducted onsite inspections. The current study proposes a crack detection method based on deep convolutional neural network (CNN) for detection of concrete cracks without explicitly calculating the defect features. In the course of the study, a database of 3200 labelled images with concrete cracks has been created, where…
Application of Segment Anything Model for Civil Infrastructure Defect Assessment
Florida Atlantic University（佛罗里达大西洋大学）；Missouri University of Science and Technology（密苏里科技大学）；University of Tehran（德黑兰大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Boston University（波士顿大学）；University of Delaware（特拉华大学）；Florida International University（佛罗里达国际大学）
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）
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…
TACK Tunnel Data (TTD): A Benchmark Dataset for Deep Learning-Based Defect Detection in Tunnels
Civil and Architectural Engineering, KTH Royal Institute of Technology（土木与建筑工程系，皇家理工学院）；Department of Civil, Building and Environmental Engineering, Sapienza University of Rome（土木、建筑与环境工程系，罗马萨皮恩扎大学）
Periodical inspection and maintenance of critical infrastructure such as dams, penstocks, and locks are of significant importance to prevent catastrophic failures. Conventional manual inspection methods require inspectors to climb along a penstock to spot corrosion, rust and crack formation which is unsafe, labor-intensive, and requires intensive training. This work presents an alternative approach using a Micro Aerial Vehicle (MAV) that autonomously flies to collect imagery which is then fed into a pretrained deep-learning model to identify corrosion. Our simplified U-Net trained with less th…
Accurate automated detection of road pavement distresses is critical for the timely identification and repair of potentially accident-inducing road hazards such as potholes and other surface-level asphalt cracks. Deployment of such a system would be further advantageous in low-resource environments where lack of government funding for infrastructure maintenance typically entails heightened risks of potentially fatal vehicular road accidents as a result of inadequate and infrequent manual inspection of road systems for road hazards. To remedy this, a recent research initiative organized by the …
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 …
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）
Al-Ahliyya Amman University（安曼阿赫利亚大学）；Budapest University of Technology and Economics（布达佩斯技术与经济大学）；Queensland University of Technology（昆士兰科技大学）；Arab American University（阿拉伯美国大学）；Columbia University（哥伦比亚大学）
