4.7 Article

Vision-based change detection for inspection of tunnel liners

Journal

AUTOMATION IN CONSTRUCTION
Volume 91, Issue -, Pages 142-154

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.autcon.2018.03.020

Keywords

Vision-based inspection; Image mosaic; Change detection

Funding

  1. ENDEAVOUR Scholarships Scheme - Government of Malta [MEDE544/2016/14]
  2. European Union - European Social Fund

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Tunnel inspections may demand personnel to access hazardous environments soliciting the need for robotic operations to minimize human intervention. CERN, the European Organisation for Nuclear Research, has a number of tunnel infrastructures, including the tunnel hosting the Large Hadron Collider (LHC). A Train Inspection Monorail (TIM) was installed in the LHC tunnel to reduce personnel intervention. It gathers data from various sensors and captures images which, up till now, were only used for data record purposes. In this paper we present a computer vision system, Tlnspect, that uses a robust hybrid change detection algorithm to monitor changes on the LHC tunnel linings. The system achieves a high sensitivity of 83.5% and 82.8% precision, and an average accuracy of 81.4%. The proposed system is also configurable through different parameters to adapt to different scenarios, making it useable in other tunnel environments and therefore not exclusive to the LHC tunnel.

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Sallese, T. Salmi, A. Salzburger, O. A. Sampayo, S. Sanfilippo, J. Santiago, E. Santopinto, R. Santoro, A. Sanz Ull, X. Sarasola, I. H. Sarpun, M. Sauvain, S. Savelyeva, R. Sawada, G. F. R. Sborlini, A. Schaffer, M. Schaumann, M. Schenk, C. Scheuerlein, I. Schienbein, K. Schlenga, H. Schmickler, R. Schmidt, D. Schoerling, T. Schoeerner-Sadenius, A. Schoning, M. Schott, D. Schulte, P. Schwaller, C. Schwanenberger, P. Schwemling, N. Schwerg, L. Scibile, A. Sciuto, E. Scomparin, C. Sebastiani, B. Seeber, M. Segreti, P. Selva, M. Selvaggi, C. Senatore, A. Senol, L. Serin, M. Serluca, N. Serra, A. Seryi, L. Sestini, A. Sfyrla, M. Shaposhnikov, E. Shaposhnikova, B. Y. Sharkov, D. Shatilov, J. Shelton, V. Shiltsev, I. P. Shipsey, G. D. Shirkov, A. Shivaji, D. Shwartz, T. Sian, S. Sidorov, L. Silvestrini, N. Simand, F. Simon, B. K. Singh, A. Siodmok, Y. Sirois, E. Sirtori, R. Sirvinskaite, B. Sitar, T. Sjoestrand, P. Skands, E. Skordis, K. Skovpen, M. Skrzypek, E. Slade, P. Slavich, R. Slovak, V. Smaluk, V. Smirnov, W. Snoeys, L. Soffi, P. Sollander, O. Solovyanov, H. K. Soltveit, H. Song, P. Sopicki, M. Sorbi, L. Spallino, M. Spannowsky, B. Spataro, P. Sphicas, H. Spiesberger, P. Spiller, M. Spira, T. Srivastava, J. Stachel, A. Stakia, J. L. Stanyard, E. Starchenko, A. Y. Starikov, A. M. Stasto, M. Statera, R. Steerenberg, J. Steggemann, A. Stenvall, F. Stivanello, D. Stockinger, L. S. Stoel, M. Stoeger-Pollach, B. Strauss, M. Stuart, G. Stupakov, S. Su, A. Sublet, K. Sugita, L. Sulak, M. K. Sullivan, S. Sultansoy, T. Sumida, K. Suzuki, G. Sylva, M. J. Syphers, A. Sznajder, M. Taborelli, N. A. Tahir, M. Takeuchi, E. Tal Hod, C. Tambasco, J. Tanaka, K. Tang, I. Tapan, S. Taroni, G. F. Tartarelli, G. Tassielli, L. Tavian, T. M. Taylor, G. N. Taylor, A. M. Teixeira, G. Tejeda-Munoz, V. I. Telnov, R. Tenchini, H. H. J. ten Kate, K. Terashi, A. Tesi, M. Testa, C. Tetrel, D. Teytelman, J. Thaler, A. Thamm, S. Thomas, M. T. Tiirakari, V. Tikhomirov, D. Tikhonov, H. Timko, V. Tisserand, L. M. Tkachenko, J. Tkaczuk, J. P. Tock, B. Todd, E. Todesco, R. Tomas Garcia, D. Tommasini, G. Tonelli, F. Toral, T. Torims, R. Torre, Z. Townsend, R. Trant, D. Treille, L. Trentadue, A. Tricoli, A. Tricomi, W. Trischuk, I. S. Tropin, B. Tuchming, A. A. Tudora, B. Turbiarz, I. Turk Cakir, M. Turri, T. Tydecks, J. Usovitsch, J. Uythoven, R. Vaglio, A. Valassi, F. Valchkova, M. A. Valdivia Garcia, P. Valente, R. U. Valente, A. -M. Valente-Feliciano, G. Valentino, L. Vale Silva, J. M. Valet, R. Valizadeh, J. W. F. Valle, S. Vallecorsa, G. Vallone, M. van Leeuwen, U. H. van Rienen, L. van Riesen-Haupt, M. Varasteh, L. Vecchi, P. Vedrine, G. Velev, R. Veness, A. Ventura, W. Venturini Delsolaro, M. Verducci, C. B. Verhaaren, C. Vernieri, A. P. Verweij, O. Verwilligen, O. Viazlo, A. Vicini, G. Viehhauser, N. Vignaroli, M. Vignolo, A. Vitrano, I. Vivarelli, S. Vlachos, M. Vogel, D. M. Vogt, V. Voelkl, P. Volkov, G. Volpini, J. von Ahnen, G. Vorotnikov, G. G. Voutsinas, V. Vysotsky, U. Wagner, R. Wallny, L. -T. Wang, R. Wang, K. Wang, B. F. L. Ward, T. P. Watson, N. K. Watson, Z. Was, C. Weiland, S. Weinzierl, C. P. Welsch, J. Wenninger, M. Widorski, U. A. Wiedemann, H. -U. Wienands, G. Wilkinson, P. H. Williams, A. Winter, A. Wohlfahrt, T. Wojton, D. Wollmann, J. Womersley, D. Woog, X. Wu, A. Wulzer, M. K. Yanehsari, G. Yang, H. J. Yang, W. -M. Yao, E. Yazgan, V. Yermolchik, A. Yilmaz, A. Yilmaz, H. -D. Yoo, S. A. Yost, T. You, C. Young, T. -T. Yu, F. Yu, A. Zaborowska, S. G. Zadeh, M. Zahnd, M. Zanetti, L. Zanotto, L. Zawiejski, P. Zeiler, M. Zerlauth, S. M. Zernov, G. Zevi Dell Porta, Z. Zhang, Y. Zhang, C. Zhang, H. Zhang, Z. Zhao, Y. -M. Zhong, J. Zhou, D. Zhou, P. Zhuang, G. Zick, F. Zimmermann, J. Zinn-Justin, L. Zivkovic, A. V. Zlobin, M. Zobov, J. Zupan, J. Zurita

EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS (2019)

Article Physics, Particles & Fields

First determination of the ρ parameter at √s=13 TeV: probing the existence of a colourless C-odd three-gluon compound state

G. Antchev, P. Aspell, I Atanassov, V Avati, J. Baechler, C. Baldenegro Barrera, V Berardi, M. Berretti, E. Bossini, U. Bottigli, M. Bozzo, R. Bruce, H. Burkhardt, F. S. Cafagna, M. G. Catanesi, M. Csanad, T. Csorgo, M. Deile, F. De Leonardis, A. D'Orazio, M. Doubek, D. Druzhkin, K. Eggert, V Eremin, F. Ferro, A. Fiergolski, F. Garcia, H. Garcia Morales, V Georgiev, S. Giani, L. Grzanka, J. Hammerbauer, J. Heino, P. Helander, T. Isidori, V Ivanchenko, M. Janda, A. Karev, J. Kavspar, J. Kopal, V Kundrat, S. Lami, G. Latino, R. Lauhakangas, R. Linhart, C. Lindsey, M. Lokajivcek, L. Losurdo, M. Lo Vetere, F. Lucas-Rodriguez, M. Macri, M. Malawski, A. Mereghetti, N. Minafra, S. Minutoli, D. Mirarchi, T. Naaranoja, F. Nemes, H. Niewiadomski, T. Novak, E. Oliveri, F. Oljemark, M. Oriunno, K. Osterberg, P. Palazzi, V Passaro, Z. Peroutka, J. Prochazka, M. Quinto, E. Radermacher, E. Radicioni, F. Ravotti, S. Redaelli, E. Robutti, C. Royon, G. Ruggiero, H. Saarikko, B. Salvachua, A. Scribano, J. Siroky, J. Smajek, W. Snoeys, R. Stefanovitch, J. Sziklai, C. Taylor, E. Tcherniaev, N. Turini, V. Vacek, G. Valentino, J. Welti, J. Wenninger, J. Williams, P. Wyszkowski, J. Zich, K. Zielinski

EUROPEAN PHYSICAL JOURNAL C (2019)

Review Physics, Nuclear

Probing LHC halo dynamics using collimator loss rates at 6.5 TeV

A. Gorzawski, R. B. Appleby, M. Giovannozzi, A. Mereghetti, D. Mirarchi, S. Redaelli, B. Salvachua, G. Stancari, G. Valentino, J. F. Wagner

PHYSICAL REVIEW ACCELERATORS AND BEAMS (2020)

Article Chemistry, Analytical

Vision-Based Tunnel Lining Health Monitoring via Bi-Temporal Image Comparison and Decision-Level Fusion of Change Maps

Leanne Attard, Carl James Debono, Gianluca Valentino, Mario Di Castro

Summary: A machine vision change detection application is proposed to improve the structural health monitoring of tunnels, reducing the need for human presence in hazardous environments, and achieving high recall, precision, and F1-score values through image processing and deep learning techniques.

SENSORS (2021)

Article Chemistry, Multidisciplinary

Hybrid Machine Learning-Statistical Method for Anomaly Detection in Flight Data

Sameer Kumar Jasra, Gianluca Valentino, Alan Muscat, Robert Camilleri

Summary: This paper investigates the use of an unsupervised hybrid statistical-local outlier factor algorithm to detect anomalies in time-series flight data. It shows that LOF quantifies the degree of outlier-ness of an outlier and allows for comparing anomalous flights. LOF also helps track anomalous behavior during a flight, providing insights into abnormal behavior for experts.

APPLIED SCIENCES-BASEL (2022)

Article Physics, Multidisciplinary

Application of reinforcement learning in the LHC tune feedback

Leander Grech, Gianluca Valentino, Diogo Alves, Simon Hirlaender

Summary: This study explores the beam-based control problem in the CERN Large Hadron Collider and improves an important code using reinforcement learning. The results from the simulation environment show that the performance of reinforcement learning agents surpasses the classical approach.

FRONTIERS IN PHYSICS (2022)

Article Computer Science, Information Systems

Use of UAVs and Deep Learning for Beach Litter Monitoring

Roland Pfeiffer, Gianluca Valentino, Sebastiano D'Amico, Luca Piroddi, Luciano Galone, Stefano Calleja, Reuben A. Farrugia, Emanuele Colica

Summary: In this paper, the authors propose an autonomous monitoring and retrieval method using drone surveys and deep learning object detection. The algorithm trained on drone footage combined with litter datasets can detect litter objects with moderate accuracy. Additionally, the geolocation of detected objects and beach morphology information provide important building blocks for an automated monitoring and retrieval pipeline.

ELECTRONICS (2023)

Article Environmental Sciences

Evaluating Characteristics of an Active Coastal Spreading Area Combining Geophysical Data with Satellite, Aerial, and Unmanned Aerial Vehicles Images

Emanuele Colica, Luciano Galone, Sebastiano D'Amico, Adam Gauci, Roberto Iannucci, Salvatore Martino, Davide Pistillo, Peter Iregbeyen, Gianluca Valentino

Summary: A methodology for evaluating cliff erosion/retreat was developed by integrating geomatics and geophysical techniques. A 3D digital model of the study area was generated using UAV photogrammetry, and geophysical measurements such as electrical resistivity tomography and ground penetrating radar were conducted. The movement and evolution of boulders and cracks in rocks were analyzed through time, providing information for qualitative assessment of coastal variations and planning risk mitigation strategies.

REMOTE SENSING (2023)

Proceedings Paper Engineering, Ocean

Detecting beach litter in drone images using deep learning

Roland Pfeiffer, Gianluca Valentino, Reuben A. Farrugia, Emanuele Colica, Sebastiano D'Amico, Stefano Calleja

Summary: Beach pollution from litter has negative effects that require mitigation and cleanup efforts. Automated monitoring using drone technology and artificial intelligence/object detection has become more feasible. In this study, two deep learning algorithms (YOLOv5 and Faster R-CNN) were trained on drone footage to monitor litter on Maltese beaches. YOLOv5 outperformed Faster R-CNN in terms of detection performance, with an average precision (mAP) of 0.542 compared to 0.328. The geolocation of detected litter objects had an average estimation error of 3.7 meters.

2022 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR THE SEA LEARNING TO MEASURE SEA HEALTH PARAMETERS (METROSEA) (2022)

Article Computer Science, Information Systems

A Machine Learning Approach for the Tune Estimation in the LHC

Leander Grech, Gianluca Valentino, Diogo Alves

Summary: New tune estimation algorithms are proposed to address the issue of inaccuracies caused by 50 Hz noise harmonics in the LHC beam, with comparative analysis conducted on a simulated dataset.

INFORMATION (2021)

Article Computer Science, Information Systems

Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade

Massimo Giovannozzi, Ewen Maclean, Carlo Emilio Montanari, Gianluca Valentino, Frederik E. Van der Veken

Summary: Machine Learning has been utilized in Science and Engineering for decades, with recent efforts focused on applying it to Accelerator Physics, particularly in the analysis of data from particle colliders and beam dynamics studies. The research aims to develop efficient algorithms for outlier detection and to improve the quality of fitted models expressing the time evolution of dynamic aperture.

INFORMATION (2021)

Article Computer Science, Information Systems

COTS: A Multipurpose RGB-D Dataset for Saliency and Image Manipulation Applications

Dylan Seychell, Carl James Debono, Mark Bugeja, Jeremy Borg, Matthew Sacco

Summary: This paper introduces an RGB-D dataset designed for applications involving salient object detection, segmentation, inpainting, and blending techniques. The dataset fills a gap in the evaluation of image inpainting and blending applications, allowing for experiments to evaluate these different applications. Results demonstrate novel possibilities for the evaluation of computer vision applications.

IEEE ACCESS (2021)

Proceedings Paper Engineering, Electrical & Electronic

A CAD System for Brain Haemorrhage Detection in Head CT Scans

John Napier, Carl James Debono, Paul Bezzina, Francis Zarb

PROCEEDINGS OF 18TH INTERNATIONAL CONFERENCE ON SMART TECHNOLOGIES (IEEE EUROCON 2019) (2019)

Proceedings Paper Telecommunications

A Cross-Layer CQI Feedback Reduction Technique for MVD Transmission in Crowd Event Scenarios

Mario Cordina, Carl J. Debono

2019 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC) (2019)

Article Construction & Building Technology

Lightweight convolutional neural network driven by small data for asphalt pavement crack segmentation

Jia Liang, Qipeng Zhang, Xingyu Gu

Summary: A lightweight PCSNet-based segmentation model is developed to address the issues of insufficient performance in feature extraction and boundary loss information. The introduction of generalized Dice loss improves prediction performance, and the visualization of class activation mapping enhances model interpretability.

AUTOMATION IN CONSTRUCTION (2024)

Article Construction & Building Technology

Contextual multimodal approach for recognizing concurrent activities of equipment in tunnel construction projects

Gilsu Jeong, Minhyuk Jung, Seongeun Park, Moonseo Park, Changbum Ryan Ahn

Summary: This study introduces a contextual audio-visual approach to recognize multi-equipment activities in tunnel construction sites, improving monitoring effectiveness. Tested against real-world operation data, the model achieved remarkable results, emphasizing the potential of contextual multimodal models in enhancing operational efficiency in complex construction sites.

AUTOMATION IN CONSTRUCTION (2024)

Article Construction & Building Technology

Dual-path network combining CNN and transformer for pavement crack segmentation

Jin Wang, Zhigao Zeng, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba, Jianming Zhang, Lei Wang

Summary: This study presents a dual-path network for pavement crack segmentation, combining Convolutional Neural Network (CNN) and transformer. A lightweight CNN encoder is used for local feature extraction, while a novel transformer encoder integrates high-low frequency attention mechanism and efficient feedforward network for global feature extraction. Additionally, a complementary fusion module is introduced to aggregate intermediate features extracted from both encoders. Evaluation on three datasets confirms the superior performance of the proposed network.

AUTOMATION IN CONSTRUCTION (2024)

Article Construction & Building Technology

Robust optimization for geometrical design of 2D sequential interlocking assemblies

Pierre Gilibert, Romain Mesnil, Olivier Baverel

Summary: This paper introduces a flexible method for crafting 2D assemblies adaptable to various geometric assumptions in the realm of sustainable construction. By utilizing digital fabrication technologies and optimization approaches, precise control over demountable buildings can be achieved, improving mechanical performance and sustainability.

AUTOMATION IN CONSTRUCTION (2024)

Review Construction & Building Technology

Breaking new ground: Opportunities and challenges in tunnel boring machine operations with integrated management systems and artificial intelligence

Jorge Loy-Benitez, Myung Kyu Song, Yo-Hyun Choi, Je-Kyum Lee, Sean Seungwon Lee

Summary: This paper discusses the advancement of tunnel boring machines (TBM) through the application of artificial intelligence. It highlights the significance of AI-based management subsystems for automatic TBM operations and presents recent contributions in this field. The paper evaluates modeling, monitoring, and control subsystems and suggests research paths for integrating existing management subsystems into TBM automation.

AUTOMATION IN CONSTRUCTION (2024)

Review Construction & Building Technology

Text mining and natural language processing in construction

Alireza Shamshiri, Kyeong Rok Ryu, June Young Park

Summary: This paper reviews the application of text mining and natural language processing in the construction field, highlighting the need for automation and minimizing manual tasks. The study identifies potential research opportunities in strengthening overlooked construction aspects, coupling diverse data formats, and leveraging pre-trained language models and reinforcement learning.

AUTOMATION IN CONSTRUCTION (2024)

Article Construction & Building Technology

Improved coverage path planning for indoor robots based on BIM and robotic configurations

Zhengyi Chen, Hao Wang, Keyu Chen, Changhao Song, Xiao Zhang, Boyu Wang, Jack C. P. Cheng

Summary: This study proposes an improved coverage path planning system that leverages building information modeling and robotic configurations to optimize coverage performance in indoor environments. Experimental validation shows the effectiveness and applicability of the system. Future research will focus on further enhancing coverage ratio and optimizing computation time.

AUTOMATION IN CONSTRUCTION (2024)

Review Construction & Building Technology

Human-robot collaboration for modular construction manufacturing: Review of academic research

Yonglin Fu, Junjie Chen, Weisheng Lu

Summary: This study presents a review of human-robot collaboration (HRC) in modular construction manufacturing (MCM), focusing on tasks, human roles, and interaction levels. The review found that HRC solutions are applicable to various MCM tasks, with a primary focus on timber component production. It also revealed the diverse collaborative roles humans can play and the varying levels of interaction with robots.

AUTOMATION IN CONSTRUCTION (2024)

Article Construction & Building Technology

Steel cable bonding in fresh mortar and 3D printed beam flexural behavior

Qiong Liu, Shengbo Cheng, Chang Sun, Kailun Chen, Wengui Li, Vivian W. Y. Tam

Summary: This paper presents an approach to enhance the path-following capability of concrete printing by integrating steel cables into the printed mortar strips, and validates the feasibility and effectiveness of this approach through experiments.

AUTOMATION IN CONSTRUCTION (2024)

Article Construction & Building Technology

A transformer and self-cascade operation-based architecture for segmenting high-resolution bridge cracks

Honghu Chu, Lu Deng, Huaqing Yuan, Lizhi Long, Jingjing Guo

Summary: The study proposes a method called Cascade CATransUNet for high-resolution crack image segmentation. This method combines the coordinate attention mechanism and self-cascaded design to accurately segment cracks. Through a customized feature extraction architecture and an optimized boundary loss function, the proposed method achieves impressive segmentation performance on HR images and demonstrates its practicality in UAV crack detection tasks.

AUTOMATION IN CONSTRUCTION (2024)

Article Construction & Building Technology

Automated production of synthetic point clouds of truss bridges for semantic and instance segmentation using deep learning models

Daniel Lamas, Andres Justo, Mario Soilan, Belen Riveiro

Summary: This paper introduces a new method for creating synthetic point clouds of truss bridges and demonstrates the effectiveness of a deep learning approach for semantic and instance segmentation of these point clouds. The proposed methodology has significant implications for the development of automated inspection and monitoring systems for truss bridges.

AUTOMATION IN CONSTRUCTION (2024)

Article Construction & Building Technology

Dynamic building defect categorization through enhanced unsupervised text classification with domain-specific corpus embedding methods

Kahyun Jeon, Ghang Lee, Seongmin Yang, Yonghan Kim, Seungah Suh

Summary: This study proposes two enhanced unsupervised text classification methods for domain-specific non-English text. The results of the tests show that these methods achieve excellent performance on Korean building defect complaints, outperforming state-of-the-art zero-shot and few-shot text classification methods, with minimal data preparation effort and computing resources.

AUTOMATION IN CONSTRUCTION (2024)

Article Construction & Building Technology

Transformer language model for mapping construction schedule activities to uniformat categories

Yoonhwa Jung, Julia Hockenmaier, Mani Golparvar-Fard

Summary: This study introduces a transformer-based natural language processing model, UNIfORMATBRIDGE, that automatically labels activities in a project schedule with Uniformat classification. Experimental results show that the model performs well in matching unstructured schedule data to Uniformat classifications. Additionally, the study highlights the importance of this method in developing new techniques.

AUTOMATION IN CONSTRUCTION (2024)

Article Construction & Building Technology

Digital twin for indoor condition monitoring in living labs: University library case study

De-Graft Joe Opoku, Srinath Perera, Robert Osei-Kyei, Maria Rashidi, Keivan Bamdad, Tosin Famakinwa

Summary: This paper introduces a digital twin technology combining Building Information Modelling and the Internet of Things for the construction industry, aiming to optimize building conditions. The technology is implemented in a university library, successfully achieving real-time data capture and visual representation of internal conditions.

AUTOMATION IN CONSTRUCTION (2024)

Article Construction & Building Technology

Learning multi-granular worker intentions from incomplete visual observations for worker-robot collaboration in construction

Zaolin Pan, Yantao Yu

Summary: The construction industry faces safety and workforce shortages globally, and worker-robot collaboration is seen as a solution. However, robots face challenges in recognizing worker intentions in construction. This study tackles these challenges by proposing a fusion method and investigating the best granularity for recognizing worker intentions. The results show that the proposed method can recognize multi-granular worker intentions effectively, contributing to seamless worker-robot collaboration in construction.

AUTOMATION IN CONSTRUCTION (2024)