Automatic pavement crack detection based on single stage salient-instance segmentation and concatenated feature pyramid network-Reference-Cited by-同舟云学术

Automatic pavement crack detection based on single stage salient-instance segmentation and concatenated feature pyramid network

Published:2021-06-11 Issue: Volume: Page:1-17
ISSN:1029-8436
Container-title:International Journal of Pavement Engineering
language:en
Short-container-title:International Journal of Pavement Engineering

Author:

Li Gang¹,Lan Dongchao¹,Zheng Xuan¹,Li Xue¹,Zhou Jian²

Affiliation:

1. School of Electronic and Control Engineering, Chang’an University, Xi’an, People’s Republic of China

2. Public Smart City and Digital Transportation Engineering Institute, China Merchants Chongqing Communications Technology Research&Design Institute Co., Ltd., Chongqing, People’s Republic of China

Funder

Key Research and Development Program of Shaanxi

Key Research and Development Program of Guangxi

Science and Technology Bureau of Xi’an project

Open Fund of the Inner Mongolia Transportation Development Research Center

Publisher

Informa UK Limited

Subject

Mechanics of Materials,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/10298436.2021.1938045

Reference48 articles.

1. Automatic Pixel-Level Pavement Crack Detection Using Information of Multi-Scale Neighborhoods

2. Billah, U.H. et al., 2019. Classification of concrete crack using deep residual networked. In Proceedings of the 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-9), St. Louis, MO, USA, 4-7.

3. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

4. Unsupervised novelty detection–based structural damage localization using a density peaks-based fast clustering algorithm

5. NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Joint Optimization of Crack Segmentation With an Adaptive Dynamic Threshold Module;IEEE Transactions on Intelligent Transportation Systems;2024-07

2. Design of novel highly sensitive sensors for crack detection in metal surfaces: theoretical foundation and experimental validation;Scientific Reports;2023-10-29

3. Drivers’ EEG Responses to Different Distraction Tasks;Automotive Innovation;2023-01-17

4. A numerical study on dynamic characteristics of 5 MW floating wind turbine under wind-rain conditions;Ocean Engineering;2022-10

5. A New Unsupervised Deep Learning Algorithm for Fine-Grained Detection of Driver Distraction;IEEE Transactions on Intelligent Transportation Systems;2022-10