CrackFormer Network for Pavement Crack Segmentation-Reference-Cited by-同舟云学术

CrackFormer Network for Pavement Crack Segmentation

Published:2023-09 Issue:9 Volume:24 Page:9240-9252
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Liu Huajun¹^ORCID,Yang Jing¹,Miao Xiangyu¹,Mertz Christoph²,Kong Hui³^ORCID

Affiliation:

1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

2. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

3. State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC), the Department of Electromechanical Engineering (EME), and the Department of Computer and Information Science (CIS), University of Macau (UM), Taipa, Macau, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10235283/10109158.pdf?arnumber=10109158

Reference52 articles.

1. Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images

2. A new minimal path selection algorithm for automatic crack detection on pavement images

3. A Deep Learning Approach for Road Damage Detection from Smartphone Images

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

5. ImageNet classification with deep convolutional neural networks;krizhevsky;Commun ACM,2012

Cited by 22 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. Enhancing pixel-level crack segmentation with visual mamba and convolutional networks;Automation in Construction;2024-12

2. A non-local based microcrack segmentation model optimized for effective high resolution and low-power devices;Journal of Building Engineering;2024-11

3. Real-time pavement surface crack detection based on lightweight semantic segmentation model;Transportation Geotechnics;2024-09

4. DUCTNet: An Effective Road Crack Segmentation Method in UAV Remote Sensing Images Under Complex Scenes;IEEE Transactions on Intelligent Transportation Systems;2024-09

5. Transformer-based thin crack segmentation: an efficient multiscale approach for automatic visual inspection;Signal, Image and Video Processing;2024-08-12