Dual-branch crack segmentation network with multi-shape kernel based on convolutional neural network and Mamba

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成果类型：

期刊论文

作者：

"Zhang, Jianming;Li, Dianwen;Zeng, Zhigao;Zhang, Rui;Wang, Jin"

通讯作者：

Jianming Zhang

作者机构：

Key Laboratory of Safety Control of Bridge Engineering, Ministry of Education (Changsha University of Science and Technology), Changsha, 410114, China

[Zeng, Zhigao] School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China

[Zhang, Rui] National Engineering Research Center of Highway Maintenance Technology, Changsha University of Science and Technology, Changsha, 410114, China

[Wang, Jin"] Sanya Institute, Hunan University of Science and Technology, Sanya, 572024, China

["Zhang, Jianming; Li, Dianwen] Key Laboratory of Safety Control of Bridge Engineering, Ministry of Education (Changsha University of Science and Technology), Changsha, 410114, China<&wdkj&>School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China

通讯机构：

[Jianming Zhang] K

Key Laboratory of Safety Control of Bridge Engineering, Ministry of Education (Changsha University of Science and Technology), Changsha, 410114, China<&wdkj&>School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China

语种：

英文

期刊：

Engineering Applications of Artificial Intelligence

ISSN：

0952-1976

年：

2025

卷：

150

页码：

110536

DOI：

10.1016/j.engappai.2025.110536

基金类别：

CRediT authorship contribution statement Jianming Zhang: Writing – original draft, Methodology, acquisition, Conceptualization. Dianwen Li: Writing – original draft, Visualization, Software, Resources, Methodology. Zhigao Zeng: Visualization, Software. Rui Zhang: Formal analysis, Data curation. Jin Wang: Supervision, Project administration.

机构署名：

本校为第一且通讯机构

院系归属：

计算机与通信工程学院

土木工程学院

摘要：

Cracks are one of the most common pavement diseases. If not promptly repaired, they will hasten the deterioration of the road. Semantic segmentation is the most convenient pavement crack detection method to assess the damage level. Convolutional neural networks (CNN) excel at extracting local spatial information, but they have limitations in capturing global contextual information. Therefore, a dual-branch crack segmentation network (DBCNet) with Mamba and multi-shape convolutional kernels is proposed. First, a dual-branch encoder is employed to extract both spatial and contextual information,...

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Dual-branch crack segmentation network with multi-shape kernel based on convolutional neural network and Mamba

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