Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types

Deep Residual Learning for Image Recognition

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ImageNet classification with deep convolutional neural networks

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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ImageNet Large Scale Visual Recognition Challenge

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Fast R-CNN

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Crack coalescence prediction and load-bearing mechanism of defective specimen based on computer vision recognition model

Dong, T., Zhu, W., Gong, W., Wang, F., Wang, Y., & Jiang, J. (2024) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2024.110373 · Cited by 14

Bolt loosening angle measurement along full range of screw exposure length based on 3D point cloud

Li, S., Le, Y., Gao, J., Li, X., & Zhao, X. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105785 · Cited by 14

Proper orthogonal decomposition(POD) dimensionality reduction combined with machine learning to predict the vibration characteristics of stay cables at different lengths

Chen, R., Min, G., Hu, M., Yang, S., & Cai, M. (2024) · Measurement · doi:10.1016/j.measurement.2024.115827 · Cited by 14

Automatic detection of typical defects in reinforced concrete bridges via YOLOv5

Ruggieri, S., Cardellicchio, A., Nettis, A., Renò, V., & Uva, G. (2024) · Procedia Structural Integrity · doi:10.1016/j.prostr.2024.09.025 · Cited by 14

LiDAR-Based Unmanned Aerial Vehicle Offshore Wind Blade Inspection and Modeling

Oliveira, A., Dias, A. M. P., dos Santos, T. C., Rodrigues, P., Martins, A., & Almeida, J. M. (2024) · Drones · doi:10.3390/drones8110617 · Cited by 14

Novel invisible markers for monitoring cracks on masonry structures

Bal, İ. E., Dais, D., Smyrou, E., & Sarhosis, V. (2021) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.124013 · Cited by 14

An overview of the application of machine learning in predictive maintenance

Trần, N. T., Triệu, H. T., Trần, V. T., Ngô, H. H., & Đào, Q. K. (2021) · Petrovietnam Journal · doi:10.47800/pvj.2021.10-05 · Cited by 16

A Wavelet Packet Transform and Convolutional Neural Network Method Based Ultrasonic Detection Signals Recognition of Concrete

Zhao, J., Hu, T., & Zhang, Q. (2022) · Sensors · doi:10.3390/s22103863 · Cited by 14

Optimized U-shape convolutional neural network with a novel training strategy for segmentation of concrete cracks

Mousavi, M., & Bakhshi, A. (2022) · Structural Health Monitoring · doi:10.1177/14759217221105647 · Cited by 14

A survey for image based methods in construction: from images to digital twins

Koulalis, I., Dourvas, N. I., Triantafyllidis, T., Ioannidis, K., Vrochidis, S., & Kompatsiaris, I. Y. (2022) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1145/3549555.3549594 · Cited by 15

Portable motorized telescope system for wind turbine blades damage detection

Carnero, A., Martín, C., & Díáz, M. (2023) · Engineering Reports · doi:10.1002/eng2.12618 · Cited by 15

Infrastructure damage assessment via machine learning approaches: a systematic review

Abedi, M., Shayanfar, J., & Al-Jabri, K. S. (2023) · Asian Journal of Civil Engineering · doi:10.1007/s42107-023-00748-5 · Cited by 16

Unsupervised Domain Adaptation Damage Identification Approach of High Arch Dams after Earthquakes

Cao, X., Liang, C., Chen, J., Li, J., Wenyan, L., Liu, H., Liu, P., Min-yong, K., & Yunqing, T. (2023) · Structural Control and Health Monitoring · doi:10.1155/2023/6349167 · Cited by 14

In-process monitoring of the ultraprecision machining process with convolution neural networks

Manjunath, K., Tewary, S., Khatri, N., & Cheng, K. (2023) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2023.2228271 · Cited by 14

Damage detection with an autonomous UAV using deep learning

Cha, Y., & Kang, D. (2018) · Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems · doi:10.1117/12.2295961 · Cited by 14

Automatic Damage Detection of Fasteners in Overhaul Processes

Taheritanjani, S., Schoenfeld, R., & Bruegge, B. (2019) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2019.8843049 · Cited by 14

Posthurricane damage assessment using satellite imagery and geolocation features

Cao, Q. D., & Choe, Y. (2023) · Risk Analysis · doi:10.1111/risa.14244 · Cited by 14

Automated Rust Removal: Rust Detection and Visual Servo Control

Tian, Y., Zhang, G., Morimoto, K., & Ma, S. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104043 · Cited by 14

Damage localization for bridges monitored within one cluster based on a spatiotemporal correlation model of strain monitoring data

Cao, J., & Liu, Y. (2022) · Structural Health Monitoring · doi:10.1177/14759217221078766 · Cited by 13

Automatic Quantitative Identification of Bridge Surface Cracks Based on Deep Learning

Yu, J., Yi, T., Zhang, S., Li, H., Wang, Y., & Mei, X.-D. (2022) · Journal of Performance of Constructed Facilities · doi:10.1061/jpcfev.cfeng-4238 · Cited by 13

Instance segmentation of soft‐story buildings from street‐view images with semiautomatic annotation

Wang, C., Hornauer, S., Yu, S. X., McKenna, F., & Law, K. H. (2022) · Earthquake Engineering & Structural Dynamics · doi:10.1002/eqe.3805 · Cited by 13

Machine learning‐based prediction of the seismic response of fault‐crossing natural gas pipelines

Zhang, W., Ayello, F., Honegger, D., Bozorgnia, Y., & Taciroğlu, E. (2023) · Earthquake Engineering & Structural Dynamics · doi:10.1002/eqe.3945 · Cited by 13

Pavement Image Enhancement in Pixel-Wise Based on Multi-Level Semantic Information

Xu, Z., Dai, Z., Sun, Z., Li, W., & Dong, S. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3301591 · Cited by 13

Assessment of geometric parameters of segmented crack on concrete building facade using deep learning

Xu, S., Tang, H. L., Wang, X., & Wang, D. (2023) · Structures · doi:10.1016/j.istruc.2023.105188 · Cited by 13

Toward Optimal Defect Detection in Assembled Printed Circuit Boards Under Adverse Conditions

Noroozi, M., Ghadermazi, J., Shah, A., & Zayas‐Castro, J. L. (2023) · IEEE Access · doi:10.1109/access.2023.3330142 · Cited by 13

PCSGAN: A Perceptual Constrained Generative Model for Railway Defect Sample Expansion From a Single Image

He, S., Jian, Z., Liu, S., Liu, J., Fang, Y., & Hu, J. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3368213 · Cited by 13

ADVANCEMENTS IN STRUCTURAL HEALTH MONITORING: A REVIEW OF MACHINE LEARNING APPROACHES FOR DAMAGE DETECTION AND ASSESSMENT

Numan, M. (2024) · International Journal for Computational Civil and Structural Engineering · doi:10.22337/2587-9618-2024-20-1-124-142 · Cited by 13

Deep neural networks for external corrosion classification in industrial above-ground storage tanks

Alviz-Meza, A., Hadechini-Meza, L. L., & Ballesteros, D. Y. P. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e34882 · Cited by 13

Intelligent classification of construction quality problems based on unbalanced short text data mining

Dan, W., Yin, K., & Wang, H. (2024) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2024.102983 · Cited by 13

Deep-learning-based longitudinal joint opening detection method for metro shield tunnel

Yu, A., & Wensheng, M. (2024) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2024.106108 · Cited by 13

Automatic detection of structural defects in tunnel lining via network pruning and knowledge distillation in YOLO

Liu, R., & Zeng, W. (2024) · Structural Health Monitoring · doi:10.1177/14759217241289066 · Cited by 13

Lightweight multi-scale encoder– decoder network with locally enhanced attention mechanism for concrete crack segmentation

Dong, S., Cao, J., Wang, Y., Ma, J., Kuang, Z., & Zhang, Z. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ada786 · Cited by 13

An unmanned aerial vehicle implemented network for real‐time crack detection in stone cladding

Huang, B., Shi, J., Guan, X., Liu, X., Yao, X., & Liu, J. (2025) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.70021 · Cited by 15

InstaDam: Open-Source Platform for Rapid Semantic Segmentation of Structural Damage

Hoskere, V., Amer, F., Friedel, D., Yang, W., Tang, Y. L., Narazaki, Y., Smith, M. D., Golparvar‐Fard, M., & Spencer, B. F. (2021) · Applied Sciences · doi:10.3390/app11020520 · Cited by 13

A Multitask Learning-Based Neural Network for Defect Detection on Textured Surfaces Under Weak Supervision

He, K., Liu, X., Liu, J., & Wu, P. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3112784 · Cited by 14

Application of Deep-Learning Architecture for Image Analysis based Corrosion Detection

Srivastava, A., Ji, G., & Singh, R. K. (2021) · International Conference on Smart Technologies, Communication and Robotics (STCR) · doi:10.1109/stcr51658.2021.9588887 · Cited by 13

Detection of Nut–Bolt Loss in Steel Bridges Using Deep Learning Techniques

Li, Z., Adamu, K., Yan, K., Xu, X.-L., Shao, P., Li, X.-H., & Bashir, H. M. (2022) · Sustainability · doi:10.3390/su141710837 · Cited by 13

High-fidelity time-series data synthesis based on finite element simulation and data space mapping

Zhang, Y., Li, Z., Hao, R., Lin, W., Li, L., & Su, D. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110630 · Cited by 15

Prediction of retaining structure deformation of ultra-deep foundation pit by empirical mode decomposition with recurrent neural networks

Liu, Z., Liu, F., Wang, Y., Zhang, Y., Sun, Z., & Zhang, M. (2023) · Environmental Earth Sciences · doi:10.1007/s12665-023-11214-5 · Cited by 15

A hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery

Khoshboresh-Masouleh, M., & Shah–Hosseini, R. (2019) · Applied Geomatics · doi:10.1007/s12518-019-00285-4 · Cited by 13

Modeling urban growth using video prediction technology: A time-dependent convolutional encoder–decoder architecture

Jaad, A., & Abdelghany, K. F. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12503 · Cited by 13

Visual Defect Inspection for Deep-Aperture Components With Coarse-to-Fine Contour Extraction

Gong, X., Su, H., Xu, D., Zhang, J., Zhang, L., & Zhang, Z. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2928347 · Cited by 13

Parallel Dilated CNN for Detecting and Classifying Defects in Surface Steel Strips in Real-Time

Ragab, K. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82193-7_11 · Cited by 13

Using the improved YOLOv11 model to enhance computer vision applications for building crack detection algorithms

Gao, X., Cao, C., & Yi, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-22160-6 · Cited by 13

Feature Concentration for Supervised and Semisupervised Learning With Unbalanced Datasets in Visual Inspection

Jang, J.-Y., & Yoon, S. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.3003622 · Cited by 12

Scientometric analysis and critical review on the application of deep learning in the construction industry

Mansoor, A., Liu, S., Ali, G. M., Bouferguène, A., & Al‐Hussein, M. (2022) · Canadian Journal of Civil Engineering · doi:10.1139/cjce-2022-0379 · Cited by 13

Using convolutional neural networks to identify illegal roofs from unmanned aerial vehicle images

Fan, C. (2023) · Architectural Engineering and Design Management · doi:10.1080/17452007.2023.2244949 · Cited by 12

High-Precision Detection Algorithm for Metal Workpiece Defects Based on Deep Learning

Xu, X., Zhang, G., Zheng, W., Zhao, A., Zhong, Y., & Wang, H. (2023) · Machines · doi:10.3390/machines11080834 · Cited by 13

DefVAE: A Defect Detection Method for Catenary Devices Based on Variational Autoencoder

Lu, T., Wang, Z., Shen, Y., Shao, X., & Tang, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3317386 · Cited by 12

Image-based Concrete Cracks Identification under Complex Background with Lightweight Convolutional Neural Network

Meng, Q., Hu, L., Wan, D., Li, M., Wu, H., Qi, X., & Tian, Y.-D. (2023) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-023-0923-1 · Cited by 12

A Binocular Vision-Based Crack Detection and Measurement Method Incorporating Semantic Segmentation

Zhang, Z., Shen, Z., Liu, J., Shu, J., & Zhang, H. (2023) · Sensors · doi:10.3390/s24010003 · Cited by 12

Research on Defect Detection Method for Composite Materials Based on Deep Learning Networks

Cheng, J., Tan, W., Yuan, Y., Zhao, Z., & Cheng, Y. (2024) · Applied Sciences · doi:10.3390/app14104161 · Cited by 12

A pyramid auxiliary supervised U-Net model for road crack detection with dual-attention mechanism

Lu, Y., Zhang, G., Duan, S., & Chen, F. (2024) · Displays · doi:10.1016/j.displa.2024.102787 · Cited by 12

Surrounding rock classification from onsite images with deep transfer learning based on EfficientNet

Zhuang, X., Fan, W., Guo, H., Chen, X., & Wang, Q. (2024) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-024-1134-7 · Cited by 12

Current Status of Image Recognition Technology in the Field of Corrosion Protection Applications

Wang, X., Zhang, W., Lin, Z., Li, H., Zhang, Y., Quan, W., Chen, Z., You, X., Zeng, Y. C., Wang, G., et al. (2024) · Coatings · doi:10.3390/coatings14081051 · Cited by 12

YOLOv8-CDD: an improved concrete defect detection method combined CNN with transformer

Wang, C., Chen, B., Li, Y., Wang, H., Tan, L., Zhang, Y., & Zhang, H. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad85f5 · Cited by 12

Quality and safety management framework for intelligent construction: Cases study in China

Dou, Y., Yan, X., Li, T., Wang, M., Zheng, R., & Yuan, Y. (2025) · KSCE Journal of Civil Engineering · doi:10.1016/j.kscej.2024.100068 · Cited by 12

A digital twin approach based method in civil engineering for classification of salt damage in building evaluation

Guzman-Torres, J. A., Domínguez-Mota, F. J., Guzmán, E. M. A., Tinoco-Guerrero, G., & Tinoco-Ruiz, J. G. (2025) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2025.02.003 · Cited by 12

Deployable Deep Learning Models for Crack Detection: Efficiency, Interpretability, and Severity Estimation

Altaf, A., Mehmood, A., Filograno, M. L., Alharbi, S. A., & Iqbal, J. (2025) · Buildings · doi:10.3390/buildings15183362 · Cited by 12

Evaluating the Work Productivity of Assembling Reinforcement through the Objects Detected by Deep Learning

Li, J., Zhao, X., Zhou, G., Zhang, M., Li, D., & Zhou, Y. (2021) · Sensors · doi:10.3390/s21165598 · Cited by 12

Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips

Luo, Q., Jiang, W., Su, J., Ai, J., & Yang, C. (2021) · Sensors · doi:10.3390/s21217264 · Cited by 12

Lightweight Neural Network for Real-Time Crack Detection on Concrete Surface in Fog

Yao, G., Sun, Y., Yang, Y., & Liao, G. (2021) · Frontiers in Materials · doi:10.3389/fmats.2021.798726 · Cited by 12

Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Wu, X., Li, J., & Wang, L. (2022) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-022-0829-x · Cited by 12

Domain adversarial training for classification of cracking in images of concrete surfaces

Santos, B. O., Valença, J., Costeira, J. P., & Júlio, E. (2022) · AI in Civil Engineering · doi:10.1007/s43503-022-00008-6 · Cited by 12

Maintenance in airport infrastructure: A bibliometric analysis and future research directions

Lahna, T., Kamsu-Foguem, B., & Abanda, H. F. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.106876 · Cited by 12

Multi-region Segmentation Pavement Crack Detection Method Based on Deep Learning

Zhang, J., Li, Y., Zhongyu, J., & Xu, S. (2023) · International Journal of Pavement Research and Technology · doi:10.1007/s42947-023-00330-x · Cited by 12

Ceiling damage detection and safety assessment in large public buildings using semantic segmentation

Han, Q., Yan, S., Wang, L., & Kawaguchi, K. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107961 · Cited by 12

Assessing the fatigue damage of concrete structures using automatically classified crack severity level information

Sun, H., Song, L., & Yu, Z. (2023) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2023.108071 · Cited by 12

Interpretability Analysis of Convolutional Neural Networks for Crack Detection

Wu, J., He, Y., Xu, C., Jia, X., Huang, Y., Chen, Q., Huang, C., Eslamlou, A. D., & Huang, S. (2023) · Buildings · doi:10.3390/buildings13123095 · Cited by 12

A Novel Hybrid Approach for Concrete Crack Segmentation Based on Deformable Oriented-YOLOv4 and Image Processing Techniques

He, Z., Su, C., & Deng, Y. (2024) · Applied Sciences · doi:10.3390/app14051892 · Cited by 12

Adversarial semi‐supervised learning method for printed circuit board unknown defect detection

Shi, W., Li, Z., Li, Y., & Liu, H. (2020) · The Journal of Engineering · doi:10.1049/joe.2019.1181 · Cited by 12

A Survey on Crack Detection Algorithms for Concrete Structures

Anitha, M. J., Hemalatha, R., & Radha, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-5029-4_53 · Cited by 12

YOLOv10-based multi-scale variant object detection for multi-category PPE non-compliance monitoring on construction sites

Wang, S., Kim, H., Yeo, J., & Kim, J. (2026) · Scientific Reports · doi:10.1038/s41598-026-53052-y · Cited by 11

A modified multiscale semantic segmentation network accounting for multi-level seismic damage features of PC structure

Yu, D., He, Z., & Ma, L. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107600 · Cited by 11

Trident-LK Net: A Lightweight Trident Structure Network With Large Kernel for Muti-Scale Defect Detection

Yang, S., Wang, X., Qian, X., Yu, Y., & Jin, W. (2023) · IEEE Access · doi:10.1109/access.2023.3333918 · Cited by 11

Dual Graph Reasoning Network for Oil Leakage Segmentation in Substation Equipment

Zhao, Z., Liu, B., Zhai, Y., Zhao, W., & Su, P. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3336439 · Cited by 11

Effect of Injection Rate and Viscosity on Stimulated Fracture in Granite: Extraction of Fracture by Convolutional Neural Network and Morphological Analysis

Park, J., Kim, Y., Kim, S. S., Kim, K. Y., & Yun, T. S. (2023) · Rock Mechanics and Rock Engineering · doi:10.1007/s00603-023-03678-5 · Cited by 11

Autonomous Site Inspection of Power Transmission Line Insulators with Unmanned Aerial Vehicle System

Ahmed, M. F., & Mohanta, J. C. (2024) · Electric Power Components and Systems · doi:10.1080/15325008.2024.2313588 · Cited by 11

Automatic Detection of Urban Pavement Distress and Dropped Objects with a Comprehensive Dataset Collected via Smartphone

Xu, L., Fu, K., Ma, T., Tang, F., & Fan, J. (2024) · Buildings · doi:10.3390/buildings14061546 · Cited by 11

Attention-based image captioning for structural health assessment of apartment buildings

Dinh, N. N. H., Shin, H., Ahn, Y., Oo, B. L., & Lim, B. T. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105677 · Cited by 12

Sparse cross-transformer network for surface defect detection

Huang, X., Li, Y., Bao, Y., & Zhu, X. (2024) · Scientific Reports · doi:10.1038/s41598-024-75680-y · Cited by 11

Deep learning-based approach for identifying vortex-induced vibrations in stay cables

Guo, J. W., Mao, R., Ma, K., & Chen, D. (2024) · Advances in Structural Engineering · doi:10.1177/13694332241295594 · Cited by 11

Post-earthquake detection of surface spalling and cracks in masonry buildings based on computer vision

Ling, L., Ma, G., Hwang, H., & Tan, X. (2025) · Structures · doi:10.1016/j.istruc.2025.109226 · Cited by 11

A Case Study on Attribute Recognition of Heated Metal Mark Image Using Deep Convolutional Neural Networks

Mao, K., Lu, D., Dazhi, E., & Tan, Z. (2018) · Sensors · doi:10.3390/s18061871 · Cited by 13

Grand Challenges of Machine-Vision Technology in Civil Structural Health Monitoring

Tang, Y., Lin, Y., Huang, X., Yao, M., Huang, Z., & Zou, X. (2020) · Artificial Intelligence Evolution · doi:10.37256/aie.112020250 · Cited by 12

Point Pattern Feature-Based Anomaly Detection for Manufacturing Defects, in the Random Finite Set Framework

Kamoona, A. M., Gostar, A. K., Bab‐Hadiashar, A., & Hoseinnezhad, R. (2021) · IEEE Access · doi:10.1109/access.2021.3130261 · Cited by 11

A Highly Efficient Surface Defect Detection Approach for Hot Rolled Strip Steel Based on Deep Learning

Hao, X., Dong, T., & Zhang, D. (2021) · International Conference on Robotics and Automation Engineering (ICRAE) · doi:10.1109/icrae53653.2021.9657815 · Cited by 11

Downstream Semantic Segmentation Model for Low-Level Surface Crack Detection

Siriborvornratanakul, T. (2022) · Advances in Multimedia · doi:10.1155/2022/3712289 · Cited by 11

Structural Damage Detection Using Convolutional Neural Networks Based on Modal Strain Energy and Population of Structures

Zhang, J., Jin, Z., Teng, S., Chen, G., & Cui, F. (2022) · International Journal of Computational Methods · doi:10.1142/s021987622230001x · Cited by 11

Pavement Cracks Segmentation Algorithm Based on Conditional Generative Adversarial Network

Kang, J., & Feng, S. (2022) · Sensors · doi:10.3390/s22218478 · Cited by 11

Defect digital twinning: A technical framework to integrate robotics, AI and BIM for facility management and renovation

Chen, J., Lu, W., Ghansah, F. A., & Peng, Z. (2022) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/1101/2/022041 · Cited by 11

Automatic segmentation of airport pavement damage by AM‐Mask R‐CNN algorithm

Zhang, H., Dong, J., & Gao, Z. (2023) · Engineering Reports · doi:10.1002/eng2.12628 · Cited by 11

A decoupled algorithm for cable parameter identification by frequency‐ratio‐offset

Sun, C., Zheng, G., Zhang, X., Cai, W., Gao, W., Chen, P., & Zhou, X. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12980 · Cited by 11

Unsupervised defect segmentation with pose priors

Midwinter, M., Al‐Sabbag, Z. A., & Yeum, C. M. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13001 · Cited by 11

Deep Learning-Based YOLO Network Model for Detecting Surface Cracks During Structural Health Monitoring

Pratibha, K., Mishra, M., Ramana, G. V., & Lourenço, P. B. (2023) · Rilem bookseries · doi:10.1007/978-3-031-39603-8_15 · Cited by 11

Scale-space effect and scale hybridization in image intelligent recognition of geological discontinuities on rock slopes

Wang, M., Wang, E., Liu, X., & Wang, C. (2023) · Journal of Rock Mechanics and Geotechnical Engineering · doi:10.1016/j.jrmge.2023.08.015 · Cited by 11

LNSNet: Lightweight Navigable Space Segmentation for Autonomous Robots on Construction Sites

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Image‐Based Underwater Inspection System for Abrasion of Stilling Basin Slabs of Dam

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