ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation

ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation

Accurate segmentation of pavement cracks from high-resolution remote sensing imagery plays a crucial role in automated road condition assessment and infrastructure maintenance. However, crack structures often exhibit asymmetry, irregular morphology, and multi-scale variations, posing significant cha...

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Bibliographic Details
Main Authors: Chao Tan, Jiaqi Liu, Zhedong Zhao, Rufei Liu, Peng Tan, Aishu Yao, Shoudao Pan, Jingyi Dong
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Applied Sciences
Subjects:
pavement crack segmentation
transformer neural networks
multi-scale feature fusion
global attention mechanism
high-resolution remote sensing
deep learning
Online Access:https://www.mdpi.com/2076-3417/15/11/6183
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