Integration of a CNN-based model and ensemble learning for detecting post-earthquake road cracks with deep features

Reis, Hatice Catal; Turk, Veysel; Karacur, Soner; Kurt, Ahmet Melih

doi:10.1016/j.istruc.2024.106179

Structures

2024

DOI: 10.1016/j.istruc.2024.106179

|View full text |Cite

Integration of a CNN-based model and ensemble learning for detecting post-earthquake road cracks with deep features

Hatice Catal Reis,

Veysel Turk,

Soner Karacur

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Vision based nighttime pavement cracks pixel level detection by integrating infrared visible fusion and deep learning

Shi,

Li,

Yao

et al. 2024

Construction and Building Materials

View full text Add to dashboard Cite

Vision based nighttime pavement cracks pixel level detection by integrating infrared visible fusion and deep learning

Shi,

Li,

Yao

et al. 2024

Construction and Building Materials

View full text Add to dashboard Cite

A semantic segmentation model for road cracks combining channel-space convolution and frequency feature aggregation

Zhang,

2024

Sci Rep

View full text Add to dashboard Cite

In transportation, roads sometimes have cracks due to overloading and other reasons, which seriously affect driving safety, and it is crucial to identify and fill road cracks in time. Aiming at the defects of existing semantic segmentation models that have degraded the segmentation performance of road crack images and the standard convolution makes it challenging to capture the spatial and channel coupling relationship between pixels. It is difficult to differentiate crack pixels from background pixels in complex backgrounds; this paper proposes a semantic segmentation model for road cracks that combines channel-spatial convolution with the aggregation of frequency features. A new convolutional block is proposed to accurately identify cracked pixels by grouping spatial displacements and convolutional kernel weight dynamization while modeling pixel spatial relationships linked to channel features. To enhance the contrast of crack edges, a frequency domain feature aggregation module is proposed, which uses a simple windowing strategy to solve the problem of mismatch of frequency domain inputs and, at the same time, takes into account the effect of the frequency imaginary part on the features to model the deep frequency features effectively. Finally, a feature refinement module is designed to refine the semantic features to improve the segmentation accuracy. Many experiments have proved that the model proposed in this paper has better performance and more application potential than the current popular general model.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Integration of a CNN-based model and ensemble learning for detecting post-earthquake road cracks with deep features

Cited by 2 publications

References 26 publications

Vision based nighttime pavement cracks pixel level detection by integrating infrared visible fusion and deep learning

Vision based nighttime pavement cracks pixel level detection by integrating infrared visible fusion and deep learning

A semantic segmentation model for road cracks combining channel-space convolution and frequency feature aggregation

Contact Info

Product

Resources

About