Improving Post-Earthquake Crack Detection using Semi-Synthetic Generated Images
Department of Electrical, Computer and Biomedical Engineering, University of Pavia（电气、计算机与生物医学工程系，帕维亚大学）；EUCENTRE Foundation, European Centre for Training and Research in Earthquake Engineering（欧洲地震工程培训与研究中心基金会）
Following an earthquake, it is vital to quickly evaluate the safety of the impacted areas. Damage detection systems, powered by computer vision and deep learning, can assist experts in this endeavor. However, the lack of extensive, labeled datasets poses a challenge to the development of these systems. In this study, we introduce a technique for generating semi-synthetic images to be used as data augmentation during the training of a damage detection system. We specifically aim to generate images of cracks, which are a prevalent and indicative form of damage. The central concept is to employ parametric meta-annotations to guide the process of generating cracks on 3D models of real-word structures. The governing parameters of these meta-annotations can be adjusted iteratively to yield images that are optimally suited for improving detectors' performance. Comparative evaluations demonstrated that a crack detection system trained with a combination of real and semi-synthetic images outperforms a system trained on real images alone.
Birla Institute of Technology & Science, Pilani（贝拉理工学院（皮拉尼））
Classification of the extent of damage suffered by a building in a seismic event is crucial from the safety perspective and repairing work. In this study, authors have proposed a CNN based autonomous damage detection model. Over 1200 images of different types of buildings-1000 for training and 200 for testing classified into 4 categories according to the extent of damage suffered. Categories are namely, no damage, minor damage, major damage, and collapse. Trained network tested by the application of various algorithms with different learning rates. The most optimum results were obtained on the…
Multiclass Post-Earthquake Building Assessment Integrating High-Resolution Optical and SAR Satellite Imagery, Ground Motion, and Soil Data with Transformers
Department of Civil and Environmental Engineering, University of Houston, TX, USA（土木与环境工程系，德克萨斯大学）；Department of Electrical and Computer Engineering, University of Houston, TX, USA（电气与计算机工程系，德克萨斯大学）；Department of Earth and Atmospheric Science, University of Houston, TX, USA（地球与大气科学系，德克萨斯大学）；German Aerospace Center (DLR), Microwaves and Radar Institute, Munich, Germany（德国航天中心（DLR）微波与雷达研究所）
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
In the aftermath of an earthquake, rapid structural inspections are required to get citizens back in to their homes and offices in a safe and timely manner. These inspections gfare typically conducted by municipal authorities through structural engineer volunteers. As manual inspec-tions can be time consuming, laborious and dangerous, research has been underway to develop methods to help speed up and increase the automation of the entire process. Researchers typi-cally envisage the use of unmanned aerial vehicles (UAV) for data acquisition and computer vision for data processing to extract act…
Silesian University of Technology（西里西亚工业大学）；Polish Academy of Sciences（波兰科学院）
The area affected by the earthquake is vast and often difficult to entirely cover, and the earthquake itself is a sudden event that causes multiple defects simultaneously, that cannot be effectively traced using traditional, manual methods. This article presents an innovative approach to the problem of detecting damage after sudden events by using an interconnected set of deep machine learning models organized in a single pipeline and allowing for easy modification and swapping models seamlessly. Models in the pipeline were trained with a synthetic dataset and were adapted to be further evalua…
Smart Transfer: Leveraging Vision Foundation Model for Rapid Building Damage Mapping with Post-Earthquake VHR Imagery
Department of Geography, National University of Singapore（新加坡国立大学地理系）；Urban Governance and Design Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）城市治理与设计学域）；School of Environment and Spatial Informatics, China University of Mining and Technology（中国矿业大学环境与空间信息学院）；Disaster Management Institute, Istanbul Technical University（伊斯坦布尔技术大学灾害管理研究所）；Department of Complexity Science and Engineering, University of Tokyo（东京大学复杂科学与工程系）；School of Automation, Southeast University（东南大学自动化学院）
A Deep Learning framework for building damage assessment using VHR SAR and geospatial data: demonstration on the 2023 Turkiye Earthquake
Department of Electrical, Computer and Biomedical Engineering, University of Pavia（帕维亚大学电气、计算机与生物医学工程系）；Department of Engineering, University of Sannio（萨诺尼奥大学工程系）；Italian Space Agency (ASI)（意大利航天局）
Zymergen（Zymergen公司）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Berkeley Seismological Laboratory（伯克利地震实验室）；AT&T（美国电话电报公司）；University of California, Berkeley（加州大学伯克利分校）
After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually l…
Graduate School of Information Sciences, Tohoku University（东北大学信息科学研究生院）；RIKEN Center for AIP（理化学研究所人工智能中心）
In this study, we consider the problem of detecting cracks from the image of a concrete surface for automated inspection of infrastructure, such as bridges. Its overall accuracy is determined by how accurately thin cracks with sub-pixel widths can be detected. Our interest is in making it possible to detect cracks close to the limit of thinness if it can be defined. Toward this end, we first propose a method for training a CNN to make it detect cracks more accurately than humans while training them on human-annotated labels. To achieve this seemingly impossible goal, we intentionally lower the…
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）
QUT Centre for Robotics（昆士兰科技大学机器人中心）；Mining3；CSIRO Mineral Resources（澳大利亚联邦科学与工业研究组织矿产资源部）
Surface cracks on buildings, natural walls and underground mine tunnels can indicate serious structural integrity issues that threaten the safety of the structure and people in the environment. Timely detection and monitoring of cracks are crucial to managing these risks, especially if the systems can be made highly automated through robots. Vision-based crack detection algorithms using deep neural networks have exhibited promise for structured surfaces such as walls or civil engineering tunnels, but little work has addressed highly unstructured environments such as rock cliffs and bare mining…
DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences（中国科学院成都山地灾害与环境研究所）；CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences（中国科学院青藏高原地球科学卓越创新中心）；University of Chinese Academy of Sciences（中国科学院大学）；Chengdu University of Technology（成都理工大学）；Sichuan Geomatics Center（四川省测绘地理信息中心）
Reinforced concrete buildings are commonly used around the world. With recent earthquakes worldwide, rapid structural damage inspection and repair cost evaluation are crucial for building owners and policy makers to make informed risk management decisions. To improve the efficiency of such inspection, advanced computer vision techniques based on convolution neural networks have been adopted in recent research to rapidly quantify the damage state of structures. In this paper, an advanced object detection neural network, named YOLO-v2, is implemented which achieves 98.2% and 84.5% average precis…
Google Research（谷歌研究院）；Stanford University（斯坦福大学）；United Nations World Food Programme（联合国世界粮食计划署）
To respond to disasters such as earthquakes, wildfires, and armed conflicts, humanitarian organizations require accurate and timely data in the form of damage assessments, which indicate what buildings and population centers have been most affected. Recent research combines machine learning with remote sensing to automatically extract such information from satellite imagery, reducing manual labor and turn-around time. A major impediment to using machine learning methods in real disaster response scenarios is the difficulty of obtaining a sufficient amount of labeled data to train a model for a…
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…
