A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

Pattern Classification

Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336

Image analysis and mathematical morphology

(1982) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(82)90078-8 · Cited by 8,152

Pattern Classification

Abe, S. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0285-4 · Cited by 3,758

Morphological Image Analysis: Principles and Applications

(2000) · Sensor Review · doi:10.1108/sr.2000.08720cae.001 · Cited by 3,537

Photo tourism

Snavely, N., Seitz, S. M., & Szeliski, R. (2006) · ACM Transactions on Graphics · doi:10.1145/1141911.1141964 · Cited by 2,977

Morphological Image Analysis

Soille, P. (1999) · Springer eBooks · doi:10.1007/978-3-662-03939-7 · Cited by 1,981

Computer Vision

Stockman, G. C., & Shapiro, L. G. (2001) · Cited by 1,706

Handbook of Image and Video Processing

Bovik, A. C. (2005) · Elsevier eBooks · doi:10.1016/b978-0-12-119792-6.x5062-1 · Cited by 1,585

CrackTree: Automatic crack detection from pavement images

Zou, Q., Cao, Y., Li, Q., Mao, Q., & Wang, S. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.11.004 · Cited by 1,131

Robust Deep Representation Learning for Road Crack Detection

Mahenge, S. F., Wambura, S. M., & Jiao, L. (2021) · International Conference on Video and Image Processing (ICVIP) · doi:10.1145/3511176.3511195 · Cited by 2

Determination of the Influence of an Absorbing Silica Gel in Concrete

Rugel, J., Zamora, D., Bonifaz, H., Durán, R. G., Delgado, V., & Toulkeridis, T. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-3-031-08288-7_2 · Cited by 2

Crack Detection in Environments with Complex Backgrounds Using Deep Convolution Neural Nets

Elmorsy, M. (2022) · Current Trends in Civil & Structural Engineering · doi:10.33552/ctcse.2022.08.000694 · Cited by 1

Road Pothole Detection Using Unmanned Aerial Vehicle Imagery and Deep Learning Technique

Ali, D. M., & Sadeq, H. A. (2022) · ZANCO Journal of Pure and Applied Sciences · doi:10.21271/zjpas.34.6.12 · Cited by 2

Surface Cracks Length Calculation in Segmented Images using Image Processing Techniques

Bolaybolay, J. M. A., Aleluya, E. R. M., Clar, S. E., & Salaan, C. J. O. (2022) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem57413.2022.10109286 · Cited by 2

Crack Detection on Asphalt Road in Malaysia using UAV Images and YOLOv4

Mahmud, M. N., Osman, M. K., Ismail, A. P., Ahmad, F., Ahmad, K. A., Ibrahim, A., & Rabiani, A. H. (2024) · IEEE International Conference on Control System, Computing and Engineering · doi:10.1109/iccsce61582.2024.10696027 · Cited by 3

Software for monitoring the state of infrastructure facilities

Gura, D. A., Kiryunikova, N. M., Lesovaya, E. D., Khusht, N., & Pavlyukova, A. P. (2020) · Herald of Dagestan State Technical University Technical Sciences · doi:10.21822/2073-6185-2020-47-3-60-70 · Cited by 2

Autonomous UAV for Building Monitoring, Detection and Localisation of FaultsPreprint

Thalanki, S., Prashant, T. V., B, H. K. M., Bhadraray, S., Aravind, S. P., Br, S., & Dhole, S. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.07166 · Cited by 2

SCNet: A Generalized Attention-based Model for Crack Fault SegmentationPreprint

Sharma, H., Pradhan, P., & Balamuralidhar, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.01426 · Cited by 2

An investigation of the condition of maintenance of facilities at public primary and high schools in Alfred Nzo East District, Eastern Cape Province, South Africa

Nqakaza, B. G. (2023) · doi:10.51415/10321/5234 · Cited by 2

Research on Pipe Surface Defect Recognition Based on Convolutional Neural Network

Yuan, Z., Guo, H., & Zhou, S. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-5073-7_11 · Cited by 1

Automatic detection of sewer defects via hierarchical deep learning

Zaheer, A. H. M., Chandra, B. B., Sai, I. N. N. K., & Sangeetha, Y. (2021) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces51350.2021.9489100 · Cited by 1

Development of Soundness Diagnostic Model for Concrete Slab Using Bridge Inspection Data

Minami, T., Fukuoka, T., Yoshikura, M., Suwa, T., & Fujiu, M. (2022) · Infrastructures · doi:10.3390/infrastructures7060082 · Cited by 1

Road Distress Detection and Classification: Harnessing the Synergy of Deep Learning and Transfer Learning Approaches

Khlifati, O., & Baba, K. (2023) · Springer proceedings in earth and environmental sciences · doi:10.1007/978-3-031-49345-4_33 · Cited by 1

Image Processing Approaches for Identifying Cracks in Concrete Structures

Kumar, C., Sinha, A. K., Anand, P., & Pandey, S. (2024) · Lecture notes in civil engineering · doi:10.1007/978-981-99-9625-4_20 · Cited by 1

RD-Crack: A Study of Concrete Crack Detection Guided by a Residual Neural Network Improved Based on Diffusion Modeling

Huang, Y., Lai, X., Wang, Z., Ye, M., Li, Y., Li, Y., Zhang, F., & Luo, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72356-8_23 · Cited by 1

Big data acquisition for underground infrastructure condition assessment

Wang, C., Xiao, Z., Wang, Y., Wang, F., & Li, Z. (2024) · Data-Centric Engineering · doi:10.1017/dce.2024.58 · Cited by 1

Unlocking Large Language Models for Project Scheduling

Zhang, Y., Li, X., Teng, Y., Shen, Q., & Bai, S. (2025) · International Conference on Construction and Real Estate Management (ICCREM) · doi:10.1061/9780784485910.053 · Cited by 1

Detailed fault detection of industrial sensor based on semantic segmentation models

Chen, X., & Liu, H. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111576 · Cited by 1

Time-Series 3D Modeling of Tunnel Damage Through Fusion of Image and Point Cloud Data

Lee, C., Kim, D., Kim, D., & Kang, J. (2025) · Remote Sensing · doi:10.3390/rs17183173 · Cited by 1

Deep internal learning-based video compressive sensing for the identification of high-frequency structural dynamic characteristics using full-field vision methods

Wang, J., Zhu, Q., Zhang, Q., Wang, X., & Du, Y. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103903 · Cited by 1

Efficient road roughness characterization using Gaussian process regression and 2D imaging

Stefanelli, R., Mavros, G., Martínez-García, M., Knowles, J. A. C., Timpone, F., & Farroni, F. (2026) · Measurement · doi:10.1016/j.measurement.2026.121590 · Cited by 1

Crack localization in RCC slabs using acoustic emission data and multilayer perceptron

Das, S., Datta, A. K., Topdar, P., Thakur, G. S. M., & Sengupta, S. (2025) · Asian Journal of Civil Engineering · doi:10.1007/s42107-025-01584-5 · Cited by 1

RSM-based optimisation of unmanned aerial vehicles (UAV) photogrammetry parameters for pavement distress assessment

Zihan, Z. U. A., Smadi, O. G., Nlenanya, I., & Al-Hamdan, A. B. (2025) · International Journal of Pavement Engineering · doi:10.1080/10298436.2025.2579646 · Cited by 1

Enhancing the resilience of urban buildings against extreme wind events: Evaluation, mitigation, and diagnostic approaches

Dias, J. M. (2025) · Sustainable Cities and Society Advances · doi:10.1016/j.scsadv.2025.100008 · Cited by 1

Path optimization for surface damage detection on large structures

Xu, Y., Diao, Z., Li, J., Liu, Z., Zhang, X., Ye, J., & Nie, J. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.115094 · Cited by 1

FACTS: Training-free zero-shot diffusion framework for facade texture restoration in 3D urban models

Cheng, J., Huang, X., Chen, G., Wang, T., Wang, J., Tan, X., Jiang, A., & Zhang, X. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104385 · Cited by 1

Unraveling the impact of machine learning on the AEC sector: academia and industry perspectives

Bloch, T., Hamo-Goren, C., Grobman, Y. J., & Austern, G. (2026) · Architectural Engineering and Design Management · doi:10.1080/17452007.2026.2632103 · Cited by 2

Multi-scale crack localization and measurement through crowdsourced image analysis for housing safety surveys

Li, H., Liu, W., Zhu, H., Huang, L., Zhong, J., Hou, Y., & Gao, K. (2026) · Engineering Structures · doi:10.1016/j.engstruct.2026.122372 · Cited by 1

Performance-based framework for rating bridges using visual inspection and fuzzy logic: application to AASHTO girder bridges under corrosion and alkali-silica reaction

Abdelmaksoud, A. M., Salili, S., & Oudah, F. (2026) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2026.2629583 · Cited by 1

A physic-guided YOLO framework for pavement deformation distress detection

Sheikholeslami, D., Golroo, A., Khodaii, A., Daneshvar, A., & Nejad, F. M. (2026) · Scientific Reports · doi:10.1038/s41598-026-45400-9 · Cited by 1

Research on road damage detection method based on multi-modal information collaborative representation and recursive reflux mechanism

Li, H., Chen, R., Nyirandayisabye, R., & Wang, J. (2026) · Advanced Engineering Informatics · doi:10.1016/j.aei.2026.104682 · Cited by 1

Automated Dam Crack Detection Using YOLOv10 and UAV Imagery for Structural Health Monitoring

Kannan, A., Narasimhan, G., Subburaj, M., Sivaraman, A. R., Sivaraman, A. K., & Velayutham, K. (2026) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3191/1/012103 · Cited by 1

A systematic review of computer vision-based assessment of concrete properties

Ullah, M., & Plevris, V. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12005-2 · Cited by 1

Bridging Image-Based Detection and Field Evaluation: A Semi-Automated Pavement Distress Assessment Framework

Sitilbay, B. D., & Yılmaz, M. O. (2026) · Sustainability · doi:10.3390/su18104935 · Cited by 1

GMFNet: A gated multi-stage fusion network for efficient sewer defect detection based on inherent pipe joint feature

He, J., Wang, Z., Ran, Y., Liu, X., Pu, X., & Wang, Z. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115492 · Cited by 1

Multimodal YOLO-based identification and 3D characterization of concrete surface defects via integrated LiDAR and photogrammetry

Zhang, L., Deng, K., Akiyama, M., Frangopol, D. M., & Xin, J. (2026) · Automation in Construction · doi:10.1016/j.autcon.2026.107035 · Cited by 1

Reimagining road pavements: Visionary approaches to future functionality and design

Sreeram, A., Palin, D., Hajj, R., Xu, X., Wang, H., Ashish, P. K., Hu, Y., & Saxena, A. (2026) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2025.09.006 · Cited by 1

Image-driven feature mining for elastic modulus prediction in asphalt concrete

Wang, C., Wang, Y., Wu, Y., Zhang, Q., Yuan, K., & Zhu, X. (2026) · Cleaner Materials · doi:10.1016/j.clema.2026.100424 · Cited by 1

A Lightweight UAV-Mounted Metrology System for Standards-Aligned Metric Crack Width Measurement in Reinforced Concrete Bridges

Zuo, H., Cespedes, R., Zaldivar, Y., Medina, D. O. X., Bedriñana, L. A., Fiestas, J., Shirzad‐Ghaleroudkhani, N., & Mei, Q. (2026) · Metrology · doi:10.3390/metrology6030058 · Cited by 1

Detecting Road Pavement Cracks Based on Acoustic Signature Analyses

Fedele, R., & Praticò, F. G. (2021) · Lecture notes in civil engineering · doi:10.1007/978-3-030-64594-6_43 · Cited by 1

Appearance Defect Detection of Injection Parts Based on Deep Learning

Yan, D., Sun, Y., & Jiang, S. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-3391-1_11 · Cited by 1

Vision-Based Approach in Contact Modelling between the Footpad of the Lander and the Analogue Representing Surface of Phobos

Cała, M., Kohut, P., Holak, K., & Wałach, D. (2021) · Sensors · doi:10.3390/s21217009 · Cited by 1

Few-Shot Learning on 3D Surface Defect Detection with PM Networks

Chen, G., Yu, H., Jiang, L., & Shang, H. (2021) · International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) · doi:10.1145/3495018.3495037 · Cited by 1

Image Based Pothole Detection System through Machine Learning

Sournalakshmi, P., Reni Hena Helan, R., Divyalaxmi, R., Kala Ranjini, D., & Rajalakshmi, H. (2022) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-4593 · Cited by 1

A Lightweight Defect Detection Algorithm Based on Channel Shuffling and Attention Mechanism

黄, 轶. (2024) · Modeling and Simulation · doi:10.12677/mos.2024.131094 · Cited by 1

Automatic Crack Detection Using Weakly Supervised Semantic Segmentation Network and Mixed-Label Training Strategy

Zhang, S., Xu, H., Zhu, X., & Xie, L. (2024) · Foundations of Computing and Decision Sciences · doi:10.2478/fcds-2024-0007 · Cited by 1

Intelligent Vehicle for Simulated Pothole Detection Using Image Processing

Garje, P., & Patle, B. K. (2023) · International Conference on Integration of Computational Intelligent System (ICICIS) · doi:10.1109/icicis56802.2023.10430301 · Cited by 1

In Situ Acoustic Emission Investigation of Asphalt Fracture Process and Damage Quantification for Different Asphalt Mixtures

Zhu, Y., Wang, H., Sun, X., Li, F., Guan, X., Huang, M., & He, J. (2024) · Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems · doi:10.1115/1.4066645 · Cited by 1

Concrete Crack Detection Using Embedded Machine Learning

Dantas, H. C., Morais, L. M. G., Bezerra, P. H. A., & Rego, R. C. B. (2024) · International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT) · doi:10.1109/inscit62583.2024.10693377 · Cited by 1

Optimally Planning Drone Trajectories to Capture 3D Gaussian Splatting Objects

Wu, C.-Y., Sun, Y.-C., Lee, C.-T., & Hsu, C.-H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-2064-7_13 · Cited by 1

Automated Adaptive Navigation Leveraging Natural Language Models in Aerospace Robotics System

Li, Z., Huang, Y., Zhu, M., Zhang, J., Chang, J., & Liu, H. (2024) · International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI) · doi:10.1109/ricai64321.2024.10911374 · Cited by 1

Self-supervised deep hash image deduplication algorithm for visual inspection of concrete

Du, H. Y., Wang, H., Jiao, Y., Shan, Y., He, Z., Zhang, X., Pan, Z., Gao, R., & Huang, H. (2025) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-025-00942-w · Cited by 1

MambaU-Light: a lightweight model for bridge surface crack segmentation based on joint distillation of multiple models

Bai, R., Luo, D. M., Chen, L., Jin, H., & Jiang, L. (2025) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2025.2505089 · Cited by 2

Spatial clustering and NLP-based analysis of defect patterns on urban façades: Implementation on NYC buildings

Shi, Z., & Ergan, S. (2025) · Cities · doi:10.1016/j.cities.2025.106182 · Cited by 1

RoadFusion: Latent Diffusion Model for Pavement Defect Detection

Aqeel, M., Bellete, K. D., & Setti, F. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-10185-3_26 · Cited by 1

Road Defect Detection in Street View Images using Texture Descriptors and Contour Maps

Chacra, D. A., Leopold, H. A., Pinto, J., Lunscher, N., Younes, G., & Zelek, J. (2016) · Journal of Computational Vision and Imaging Systems · doi:10.15353/vsnl.v2i1.94 · Cited by 1

Causes of structural defects to bus rapid transit (BRT) facilities: Example of Xiamen BRT system

Ruan, Y., & Xu, W. A. (2018) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2018.06.230 · Cited by 1

Research on Key Feature Extraction and Position Accurate Tracking Based on Computer Vision Image

Sun, Y. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1168/4/042004 · Cited by 1

Automation in Structural Health Monitoring of Transport Infrastructure

Zhang, L., Herath, N., Raja, B. N. K., Chen, S.-L., Miramini, S., & Duffield, C. F. (2021) · Advances in 21st century human settlements · doi:10.1007/978-981-15-8670-5_6 · Cited by 1

Road Crack Detection and Recognition Based on Image Correction of Horizontal Camera and Convolutional Neural Network

Che, D., Liu, H., Luo, J., Tang, H., & Zhang, Y. (2020) · IEEE International Conference on Progress in Informatics and Computing · doi:10.1109/pic50277.2020.9350777 · Cited by 1

Building vibration monitoring based on digital optical cameras

Wang, W., & Shao, Y. (2021) · Journal of Vibroengineering · doi:10.21595/jve.2021.21999 · Cited by 1

Determination of Unsound Concrete using Non-Destructive Testing in a Smooth Concrete through various Image Processing Techniques

Dapiton, R. M., Gonzaga, J. R. P., & Garcia, R. G. (2021) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem54116.2021.9731921 · Cited by 1

Deep Learning Sobriety Monitoring System in Road-driven Car Driving Risk Assessment Pipeline

Rundo, F., & Battiato, S. (2022) · International Conference on Circuits, Systems and Simulation (ICCSS) · doi:10.1109/iccss55260.2022.9802349 · Cited by 1

PAVEMENT DEFECT CLASSIFICATION AND LOCALIZATION USING WEAKLY SUPERVISED DEEP LEARNING

Jamali, A., Laflamme, C., Huber, R., & Hammad, A. (2023) · Creative Construction Conference (CCC) · doi:10.3311/ccc2023-067 · Cited by 1

A Deep Learning-Based Object Detection Framework for Automatic Asphalt Pavement Patch Detection Using Laser Profiling Images

Syed, I. H., McKeever, S., Feighan, K., Power, D., & O’Sullivan, D. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44137-0_18 · Cited by 1

End Face Attitude Detection of Special Steel Bars Based on Improved DBSCAN

Li, Z., Zhang, J., Wang, T., Shi, W., Xiong, X., & Huang, Q. (2023) · Applied Sciences · doi:10.3390/app132312640 · Cited by 1

Worker Action Recognition in High Density Conditions based on Edge Device – Cloud Infrastructure

Nguyen, H.-T., Le, H.-V., Nguyen, B.-T., Nguyen, T.-H., & Pham, V.-C. (2023) · International Conference on Applying New Technology in Green Buildings (ATiGB) · doi:10.1109/atigb59969.2023.10364341 · Cited by 1

A Cost Effective Solution for Road Crack Inspection using Cameras and Deep Neural NetworksPreprint

Mei, Q., & Gül, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.06014 · Cited by 1

Automated Bridge Inspection for Concrete Surface Defect Detection Using Deep Neural Network Based on LiDAR Scanning

Nasrollahi, M. (2019) · Spectrum Research Repository (Concordia University) · Cited by 1

Identification of Top-down, Bottom-up, and Cement-Treated Reflective Cracks Using Convolutional Neural Network and Artificial Neural Network

Dhakal, N. (2020) · doi:10.31390/gradschool_dissertations.5270 · Cited by 1

A Novel Framework for Boundary-Preserving Semantic Segmentation of Cracks Using Multisource Visual Fusion (Mvf) and Attention-Based Hierarchical Objective Refinement MethodPreprint

Zaheer, Q., Qiu, S., Shah, S. M. A. H., Ai, C., & Wang, J. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4680261 · Cited by 1

Crackmamba: Topology-Aware Mamba for Crack Segmentation in StructuresPreprint

Zuo, X., Yu, S., Shen, J., & Shan, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4873613 · Cited by 1

Automatic evaluation of tunnel face stability based on generative large language models and multimodal knowledge graphsPreprint

Wu, C., Huang, H., Wu, C., Yu, Z., Zhang, Y., Chen, S., & Chen, J. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5870723 · Cited by 1

Software for optical recognition of micro- and nano-objects in solids and colloidal solutions

Val, O., Diachenko, L., Minov, E., Остапов, С. Е., Fochuk, P. M., Khalavka, Y. B., & Kopach, O. V. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2228627

Feature Space Augmentation: Improving Prediction Accuracy of Classical Problems in Cognitive Science and Computer Vison

Saxena, P. (2017) · e-Publications@Marquette (Marquette University)

Identificação automática do comportamento do tráfego a partir de imagens de vídeo

Marcomini, L. A. (2018) · doi:10.11606/d.18.2018.tde-01102018-102649

Use Cases for Owners and Maintainers

Kasireddy, V., Wei, Y., & Akinci, B. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815503-5.00004-8

Battery Panel Defect Detection Method Based on Deep Convolutional Neural Network

Jiang, S., Wang, T., Zhang, S., Wang, W., & Wang, H. (2019) · International Conference on Wireless Communications and Signal Processing (WCSP) · doi:10.1109/wcsp.2019.8928008

The Role of Parallelism for Rust Defect Recognition Using Digital Image Processing

Sahu, M., Jani, T., Saijahnavi, M., Kumar, A., & Dilliwar, V. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1084-7_37

Human-Centered Automation for Resilience in Acquiring Construction Field Information

Zhang, C. (2017)

Concrete Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds

Hadavandsiri, Z. (2020) · doi:10.11575/prism/37803

The Gaussian TransformPreprint

Jin, K., Mémoli, F., & Wan, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.11698 · Cited by 1

Sub-pixel displacement measurement based on Taylor expansion theory

Zhu, C., Lei, X., Guo, J., & Shen, Y. (2020) · Journal of Computational Methods in Sciences and Engineering · doi:10.3233/jcm-204501

MODERNIZAÇÃO DA INSPEÇÃO E AVALIAÇÃO DE ESTRUTURAS: AS CONTRIBUIÇÕES DE VANTS E ROBÔS NA ÁREA DE PATOLOGIA DAS CONSTRUÇÕES

Santos, R. F., SILVA, R. C., Santos, R. R., Ribeiro, J. C. L., & Mendes, B. C. (2020) · >Congresso Brasileiro de Patologia das Construções · doi:10.4322/cbpat.2020.097

Enhanced Concrete Bridge Assessment Using Artificial Intelligence and Mixed Reality

Karaaslan, E. (2019) · Journal of International Crisis and Risk Communication Research

Industrial Informatics for Quality Assurance and Real-Time Defect Detection Through Computer Vision

Marques, G., & Pitarma, R. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5029-4_27

Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment

Wang, D., Feng, J., Zhao, X., Bai, Y., Wang, Y., & Liu, X. (2020) · Geofluids · doi:10.1155/2020/8873315

Neural network fast-classifies biological images using features selected after their random-forests-importance to power smart microscopyPreprint

Balluet, M., Sizaire, F., Habouz, Y. E., Walter, T., Pont, J., Giroux, B., Bouchareb, O., Tramier, M., & Pécréaux, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.10.376988

Avaliação do desempenho de redes neurais convolucionais para detecção automática de trincas em pavimentos

Rosa, F. D., Favretto, L. D., Rodrigues, V. B., & Gharaibeh, N. G. (2020) · Transportes · doi:10.14295/transportes.v28i5.2283

High-Spatial-Resolution Scanning LIDAR: Towards Accurate Inspection of Tunnel Inner Wall

Murakami, T., Saito, N., Michikawa, T., Komachi, Y., Okamura, K., Sakashita, M., Kogure, S., Kase, K., Wada, S., & Midorikawa, K. (2017) · The Review of Laser Engineering · doi:10.2184/lsj.45.7_403

IMPROVEMENT OF EFFICIENCY AND PRECISION OF CRACK EXTRACTION METHOD USING DEEP LEARNING MODELS

Fujita, Y., Kobayashi, K., & Hamamoto, Y. (2021) · Journal of Japan Society of Civil Engineers Ser F3 (Civil Engineering Informatics) · doi:10.2208/jscejcei.77.1_14

Investigation of Strategic Deployment Opportunities for Unmanned Aerial Systems (UAS) at INDOT

Hubbard, S. M. L., & Hubbard, B. (2020) · doi:10.5703/1288284317126

Pavement distress detection with conventional self-driving car sensors

Tarabay, N. (2020) · Espace ÉTS (ETS)

Application of Deep Convolutional Neural Networks for automated and rapid identification and characterization of thin cracks in SHCCsPreprint

Das, A. K., Leung, C. K. Y., & Wan, K. T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.00230

A Study of Point Laser for Automatic Local Road Condition Assessment: Feasibility, Challenges, and Limitations

Li, W., Burrow, M. P. N., Metje, N., & Ghataora, G. S. (2021) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2021.3082505

Developing a modern recycled mix design and automatic pavement monitoring system

Majidifard, H. (2021) · doi:10.32469/10355/85799

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