Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V
Yue Fei, Kelvin C. P. Wang, Allen Zhang, et al.
IEEE Transactions on Intelligent Transportation Systems (2019) Vol. 21, Iss. 1, pp. 273-284
Closed Access | Times Cited: 263

Showing 26-50 of 263 citing articles:

A lightweight encoder–decoder network for automatic pavement crack detection
Guijie Zhu, Jiacheng Liu, Zhun Fan, et al.
Computer-Aided Civil and Infrastructure Engineering (2023) Vol. 39, Iss. 12, pp. 1743-1765
Open Access | Times Cited: 42
Densely connected deep neural network considering connectivity of pixels for automatic crack detection
Qipei Mei, Mustafa Gül, Riasat Azim
Automation in Construction (2019) Vol. 110, pp. 103018-103018
Closed Access | Times Cited: 143
Pavement crack image acquisition methods and crack extraction algorithms: A review
Weixing Wang, Mengfei Wang, Hongxia Li, et al.
Journal of Traffic and Transportation Engineering (English Edition) (2019) Vol. 6, Iss. 6, pp. 535-556
Open Access | Times Cited: 118
Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features
Song Wei-dong, Guohui Jia, Hong Zhu, et al.
Journal of Advanced Transportation (2020) Vol. 2020, pp. 1-11
Open Access | Times Cited: 110
A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain
Eric Ke Wang, Chien‐Ming Chen, Mohammad Mehedi Hassan, et al.
Future Generation Computer Systems (2020) Vol. 108, pp. 135-144
Closed Access | Times Cited: 101
Deep learning-based roadway crack classification using laser-scanned range images: A comparative study on hyperparameter selection
Shanglian Zhou, Wei Song
Automation in Construction (2020) Vol. 114, pp. 103171-103171
Closed Access | Times Cited: 97
Imaging-based crack detection on concrete surfaces using You Only Look Once network
Jianghua Deng, Ye Lü, Vincent C. S. Lee
Structural Health Monitoring (2020) Vol. 20, Iss. 2, pp. 484-499
Closed Access | Times Cited: 93
Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms
Habib Ahmed, Hung Manh La, Nenad Gucunski
Sensors (2020) Vol. 20, Iss. 14, pp. 3954-3954
Open Access | Times Cited: 92
Automatic railroad track components inspection using real‐time instance segmentation
Feng Guo, Yu Qian, Yunpeng Wu, et al.
Computer-Aided Civil and Infrastructure Engineering (2020) Vol. 36, Iss. 3, pp. 362-377
Closed Access | Times Cited: 88
Research and applications of artificial neural network in pavement engineering: A state-of-the-art review
Xu Yang, Jinchao Guan, Ling Ding, et al.
Journal of Traffic and Transportation Engineering (English Edition) (2021) Vol. 8, Iss. 6, pp. 1000-1021
Open Access | Times Cited: 87
Crack Detection from a Concrete Surface Image Based on Semantic Segmentation Using Deep Learning
Tatsuro Yamane, Pang-jo CHUN
Journal of Advanced Concrete Technology (2020) Vol. 18, Iss. 9, pp. 493-504
Open Access | Times Cited: 84
A Crack Detection Algorithm for Concrete Pavement Based on Attention Mechanism and Multi-Features Fusion
Zhong Qu, Wen Chen, Shiyan Wang, et al.
IEEE Transactions on Intelligent Transportation Systems (2021) Vol. 23, Iss. 8, pp. 11710-11719
Closed Access | Times Cited: 84
Automatic Pavement Crack Detection and Classification Using Multiscale Feature Attention Network
Song Wei-dong, Guohui Jia, Di Jia, et al.
IEEE Access (2019) Vol. 7, pp. 171001-171012
Open Access | Times Cited: 83
Machine vision-based surface crack analysis for transportation infrastructure
Wenbo Hu, Weidong Wang, Chengbo Ai, et al.
Automation in Construction (2021) Vol. 132, pp. 103973-103973
Closed Access | Times Cited: 82
Pavement Crack Detection Method Based on Deep Learning Models
Guo X. Hu, Bao Long Hu, Zhong Yang, et al.
Wireless Communications and Mobile Computing (2021) Vol. 2021, Iss. 1
Open Access | Times Cited: 81
Automated crack severity level detection and classification for ballastless track slab using deep convolutional neural network
Weidong Wang, Wenbo Hu, Wenjuan Wang, et al.
Automation in Construction (2021) Vol. 124, pp. 103484-103484
Closed Access | Times Cited: 78
Pixel-level pavement crack segmentation with encoder-decoder network
Youzhi Tang, Allen Zhang, Lei Luo, et al.
Measurement (2021) Vol. 184, pp. 109914-109914
Closed Access | Times Cited: 69
Real‐time automatic crack detection method based on drone
Shiqiao Meng, Zhiyuan Gao, Ying Zhou, et al.
Computer-Aided Civil and Infrastructure Engineering (2022) Vol. 38, Iss. 7, pp. 849-872
Closed Access | Times Cited: 61
Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance
Shanglian Zhou, Carlos Canchila, Wei Song
Automation in Construction (2022) Vol. 146, pp. 104678-104678
Closed Access | Times Cited: 60
Semantic segmentation of cracks: Data challenges and architecture
Fabio Panella, Aldo Lipani, J. Boehm
Automation in Construction (2022) Vol. 135, pp. 104110-104110
Open Access | Times Cited: 59
Automatic pixel-level crack segmentation in images using fully convolutional neural network based on residual blocks and pixel local weights
Ali Raza, Joon Huang Chuah, Mohamad Sofian Abu Talip, et al.
Engineering Applications of Artificial Intelligence (2021) Vol. 104, pp. 104391-104391
Closed Access | Times Cited: 58
A review of the research and application of deep learning-based computer vision in structural damage detection
Lingxin Zhang, Junkai Shen, Zhu Baijie
Earthquake Engineering and Engineering Vibration (2022) Vol. 21, Iss. 1, pp. 1-21
Closed Access | Times Cited: 58
Multi-scale feature fusion network for pixel-level pavement distress detection
Jingtao Zhong, Junqing Zhu, Ju Huyan, et al.
Automation in Construction (2022) Vol. 141, pp. 104436-104436
Closed Access | Times Cited: 58
Intelligent pixel‐level detection of multiple distresses and surface design features on asphalt pavements
Allen Zhang, Kelvin C. P. Wang, Yang Liu, et al.
Computer-Aided Civil and Infrastructure Engineering (2022) Vol. 37, Iss. 13, pp. 1654-1673
Closed Access | Times Cited: 55
RUC-Net: A Residual-Unet-Based Convolutional Neural Network for Pixel-Level Pavement Crack Segmentation
Gui Yu, Juming Dong, Yihang Wang, et al.
Sensors (2022) Vol. 23, Iss. 1, pp. 53-53
Open Access | Times Cited: 52

Scroll to top