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 evaluated and used with unmanned aerial vehicles (UAVs) in real-world conditions. Thanks to the methods presented in the article, it is possible to obtain high accuracy in detecting buildings defects, segmenting constructions into their components and estimating their technical condition based on a single drone flight.
Robust Lightweight Crack Classification for Real-Time UAV Bridge Inspection
Bay Area Super Bridge Maintenance Technology Center, Guangdong Provincial Highway Construction Co., Ltd., Guangdong, China（湾区超级桥梁维护技术中心、广东省高速公路建设有限公司、广东，中国）；Guangdong AIHISUN Technology Co., Ltd., Guangdong, China（广东AIHISUN技术有限公司、广东，中国）
Forschungszentrum Jülich GmbH（于利希研究中心）；Helmholtz-Institute Erlangen-Nuremberg for Renewable Energies (HI ERN)（埃尔朗根-纽伦堡亥姆霍兹可再生能源研究所）；Institute Materials for Electronics and Energy Technology, Universität Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡大学电子与能源技术材料研究所）
Increasing deployment of photovoltaics (PV) plants demands for cheap and fast inspection. A viable tool for this task is thermographic imaging by unmanned aerial vehicles (UAV). In this work, we develop a computer vision tool for the semi-automatic extraction of PV modules from thermographic UAV videos. We use it to curate a dataset containing 4.3 million IR images of 107842 PV modules from thermographic videos of seven different PV plants. To demonstrate its use for automated PV plant inspection, we train a ResNet-50 to classify ten common module anomalies with more than 90 % test accuracy. E…
Beyond Balanced Accuracy: A Resolution and Parity-Controlled Benchmark for Vision-Language and Vision-Only Defect Assessment in UAV Power-Line Inspection
State Grid Sichuan Electric Power Research Institute（国网四川省电力公司电力科学研究院）；Power System Security and Operation Key Laboratory of Sichuan Province（四川省电力系统安全与运行重点实验室）
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…
How Far are Modern Trackers from UAV-Anti-UAV? A Million-Scale Benchmark and New Baseline
Shanghai Jiao Tong University（上海交通大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；CloudWalk Technology Co., Ltd（云walk科技有限公司）；School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；School of Aeronautics and Astronautics, Sun Yat-sen University（中山大学航空宇航学院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
The usage of Unmanned Aerial Vehicles (UAVs) in the context of structural health inspection is recently gaining tremendous popularity. Camera mounted UAVs enable the fast acquisition of a large number of images often used for mapping, 3D model reconstruction, and as an assisting tool for inspectors. Due to the number of images captured during large scale UAV surveys, a manual image-based inspection analysis of entire assets cannot be efficiently performed by qualified engineers. Additionally, comparing defects to past inspections requires the retrieval of relevant images which is often impract…
UAVs have been widely used in visual inspections of buildings, bridges and other structures. In either outdoor autonomous or semi-autonomous flights missions strong GPS signal is vital for UAV to locate its own positions. However, strong GPS signal is not always available, and it can degrade or fully loss underneath large structures or close to power lines, which can cause serious control issues or even UAV crashes. Such limitations highly restricted the applications of UAV as a routine inspection tool in various domains. In this paper a vision-model-based real-time self-positioning method is …
The Chinese University of Hong Kong（香港中文大学）；Minjiang University（闽江学院）
Automatic crack detection and segmentation play a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG, and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and its performance of it is compared with other state-of-the-art network architecture. We have summarized the existin…
Vietnam National University（越南国立大学）；University of Technology Sydney（悉尼科技大学）
In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to…
International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；Indian Institute of Technology, Kharagpur（印度理工学院卡拉格普尔分校）；University of Petroleum and Energy Studies（石油与能源研究大学）
Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes. The vulnerability of a building to earthquake can be assessed through inspection that takes into account the expected damage progression of the associated component and the component's contribution to structural system performance. Most of these inspections are done manually, leading to high utilization of manpower, time, and cost. This paper proposes a methodology to auto…
This study presents an inspecting system using real-time control unmanned aerial vehicles (UAVs) to investigate structural surfaces. The system operates under favourable weather conditions to inspect a target structure, which is the Wentworth light rail base structure in this study. The system includes a drone, a GoPro HERO4 camera, a controller and a mobile phone. The drone takes off the ground manually in the testing field to collect the data requiring for later analysis. The images are taken through HERO 4 camera and then transferred in real time to the remote processing unit such as a grou…
Benchmarking CNN and Transformer-Based Object Detectors for UAV Solar Panel Inspection
Faculty of Engineering, University of Moratuwa（摩图瓦大学工程学院）；STEM College, School of Computing Technologies, RMIT University（皇家墨尔本理工学院计算机技术学院）；School of Engineering, University of Southern Queensland（南方昆士兰大学工程学院）
Institute for Infocomm Research (IR), A*STAR（新加坡科技研究局资讯通信研究院）；Nanyang Technological University (NTU)（南洋理工大学）；University of Illinois at Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）；Singapore University of Technology and Design (SUTD)（新加坡科技设计大学）
Advances in Unmanned Aerial Vehicle (UAV) opens venues for application such as tunnel inspection. Owing to its versatility to fly inside the tunnels, it can quickly identify defects and potential problems related to safety. However, long tunnels, especially with repetitive or uniform structures pose a significant problem for UAV navigation. Furthermore, post-processing visual data from the camera mounted on the UAV is required to generate useful information for the inspection task. In this work, we design a UAV with a single rotating camera to accomplish the task. Compared to other platforms, …
The condition assessment of road surfaces is essential to ensure their serviceability while still providing maximum road traffic safety. This paper presents a robust stereo vision system embedded in an unmanned aerial vehicle (UAV). The perspective view of the target image is first transformed into the reference view, and this not only improves the disparity accuracy, but also reduces the algorithm's computational complexity. The cost volumes generated from stereo matching are then filtered using a bilateral filter. The latter has been proved to be a feasible solution for the functional minimi…
Method of UAV Inspection of Photovoltaic Modules Using Thermal and RGB Data Fusion
Khmelnytskyi National University（赫梅利尼茨基国立大学）；Department of Informatics and Teleinformatics（信息学与电信学系）；Kazimierz Pulaski University of Ra-dom（卡齐米日·波尼亚图夫斯基大学）；National Academy of the State Border Service of Ukraine named after Bogdan Khmelnitsky（以博丹·赫梅利尼茨基命名的乌克兰国家边境服务学院）
Transmission Line Defect Detection Based on UAV Patrol Images and Vision-language Pretraining
Department of Electronic and Communication Engineering, North China Electric Power University（电子与通信工程系，华北电力大学）；Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University（河北省电力物联网技术重点实验室，华北电力大学）；Computer Science and Software Engineering Department, Monmouth University（计算机科学与软件工程系，蒙特莫恩大学）
Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco（Voxar Labs，伯南布哥联邦大学信息中心）；Universidade Federal Rural de Pernambuco（伯南布哥联邦农村大学）；In Forma Software（In Forma Software 公司）；Sistema de Transmissão Nordeste - STN（东北输电系统公司）
Many power line companies are using UAVs to perform their inspection processes instead of putting their workers at risk by making them climb high voltage power line towers, for instance. A crucial task for the inspection is to detect and classify assets in the power transmission lines. However, public data related to power line assets are scarce, preventing a faster evolution of this area. This work proposes the Power Line Assets Dataset, containing high-resolution and real-world images of multiple high-voltage power line components. It has 2,409 annotated objects divided into five classes: tr…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Current methods of practice for inspection of civil infrastructure typically involve visual assessments conducted manually by trained inspectors. For post-earthquake structural inspections, the number of structures to be inspected often far exceeds the capability of the available inspectors. The labor intensive and time consuming natures of manual inspection have engendered research into development of algorithms for automated damage identification using computer vision techniques. In this paper, a novel damage localization and classification technique based on a state of the art computer visi…
Bridge Damage Detection from Low-Light UAV Imagery via Degradation-Aware Mixture-of-Experts Enhancement
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）；Sustainability X-Lab, The University of Hong Kong（香港大学可持续发展X实验室）；School of Architecture, Building, and Civil Engineering, Loughborough University（拉夫堡大学建筑、建造与土木工程学院）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（香港科技大学土木与环境工程系）
Within commercial wind energy generation, the monitoring and predictive maintenance of wind turbine blades in-situ is a crucial task, for which remote monitoring via aerial survey from an Unmanned Aerial Vehicle (UAV) is commonplace. Turbine blades are susceptible to both operational and weather-based damage over time, reducing the energy efficiency output of turbines. In this study, we address automating the otherwise time-consuming task of both blade detection and extraction, together with fault detection within UAV-captured turbine blade inspection imagery. We propose BladeNet, an applicati…
With the wide applications of Unmanned Aerial Vehicle (UAV) in engineering such as the inspection of the electrical equipment from distance, the demands of efficient object detection algorithms for abundant images acquired by UAV have also been significantly increased in recent years. In this work, we study the performance of the region-based CNN for the electrical equipment defect detection by using the UAV images. In order to train the detection model, we collect a UAV images dataset composes of four classes of electrical equipment defects with thousands of annotated labels. Then, based on t…
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）
SpatialFly: Implicit 3D Prior-Guided Visual Reparameterization for Continuous UAV Vision-and-Language Navigation
School of Mechanical Engineering, Beijing Institute of Technology（北京理工大学机械工程学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Tsinghua University（清华大学）；Department of Automation, Tsinghua University（清华大学自动化系）；School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；Jianghan University（江汉大学）
