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Innovative Low-Carbon Concrete Pavements: Proof of concept project

Mohamed Heweidak (13931319), Bidur Kafle (13061115), Riyadh Al-Ameri (13057383)   +2 more
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DMA-Net: DeepLab With Multi-Scale Attention for Pavement Crack Segmentation

IEEE transactions on intelligent transportation systems (Print), 2022
Cracks are important indicators of pavement structural and operational conditions. Early pavement crack detection and treatments can help extend pavement service life, reduce fuel consumption, and improve safety and ride quality.
Xinzi Sun, Yuanchang Xie, Liming Jiang, Yu Cao, Benyuan Liu   +4 more
semanticscholar   +1 more source

Multiscale Attention Networks for Pavement Defect Detection

IEEE Transactions on Instrumentation and Measurement, 2023
Pavement defects such as cracks, net cracks, and pit slots can cause potential traffic safety problems. The timely detection and identification play a key role in reducing the harm of various pavement defects. Particularly, the recent development in deep
Junde Chen, Yuxin Wen, Y. A. Nanehkaran, Defu Zhang, Adan Zeb   +4 more
semanticscholar   +1 more source

Automatic Detection and Counting System for Pavement Cracks Based on PCGAN and YOLO-MF

IEEE transactions on intelligent transportation systems (Print), 2022
The regular detection of pavement cracks is critical for life and property security. However, existing deep learning-based methods of crack detection face difficulties in terms of data acquisition and defect counting.
Duo Ma, Hongyuan Fang, Niannian Wang, C. Zhang, Jiaxiu Dong, Haobang Hu   +5 more
semanticscholar   +1 more source

Pavement distress detection and classification based on YOLO network

International Journal of Pavement Engineering, 2020
The detection and classification of pavement distress (PD) play a critical role in pavement maintenance and rehabilitation. Research on PD automation detection and measurement has been actively conducted.
Yuchuan Du, Ning Pan, Zihao Xu, F. Deng, Yu Shen, Hua Kang   +5 more
semanticscholar   +1 more source

Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimised models

International Journal of Pavement Engineering, 2022
Resilient modulus ($M_R$MR) plays the most critical role in the evaluation and design of flexible pavement foundations. $M_R$MR is utilised as the principal parameter for representing stiffness and behaviour of flexible pavement foundation in ...
Reza Sarkhani Benemaran, M. Esmaeili‐Falak, A. Javadi   +2 more
semanticscholar   +1 more source

Pixel-Level patch detection from full-scale asphalt pavement images based on deep learning

International Journal of Pavement Engineering, 2023
The patch recognition and localisation from high-resolution pavement images play a key role in evaluating asphalt pavement condition. To achieve the purpose, two line-scan cameras were employed to acquire lots of full-scale asphalt pavement images with 1
Xue-Lu Xiong, Yiqiu Tan
semanticscholar   +1 more source

Automated pavement crack detection and segmentation based on two‐step convolutional neural network

Comput. Aided Civ. Infrastructure Eng., 2020
Cracking is a common pavement distress that would cause further severe problems if not repaired timely, which means that it is important to accurately extract the information of pavement cracks through detection and segmentation. Automated pavement crack
Jingwei Liu, Xu Yang, Stephen L. H. Lau, Xin Wang, S. Luo, V. C. Lee, Ling Ding   +6 more
semanticscholar   +1 more source

Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V

IEEE transactions on intelligent transportation systems (Print), 2020
A few recent developments have demonstrated that deep-learning-based solutions can outperform traditional algorithms for automated pavement crack detection.
Yue Fei, Kelvin C. P. Wang, Allen A. Zhang, Cheng Chen, J. Li, Yang Liu, Guangwei Yang, Baoxian Li   +7 more
semanticscholar   +1 more source

Automated pavement distress segmentation on asphalt surfaces using a deep learning network

International Journal of Pavement Engineering, 2022
Recently, many deep learning methods have achieved great results in the field of automated pavement distress detection, but most of them ignore other types of distresses beyond cracks.
Tian Wen, Shuo Ding, H. Lang, J. Lu, Ye Yuan, Yichuan Peng, Jiang Chen, Aidi Wang   +7 more
semanticscholar   +1 more source