A Multi-Resolution Attention U-Net for Pavement Distress Segmentation in 3D Images: Architecture and Data-Driven Insights

Haitao Gong, Jueqiang Tao, Xiaohua Luo and Feng Wang ()
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Haitao Gong: Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA
Jueqiang Tao: College of Engineering, Zhejiang Normal University, Jinhua 321004, China
Xiaohua Luo: Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA
Feng Wang: Ingram School of Engineering, Texas State University, San Marcos, TX 78666, USA

Mathematics, 2025, vol. 13, issue 17, 1-18

Abstract: High-resolution 3D pavement images have become a valuable data source for automated surface distress detection and assessment. However, accurately identifying and segmenting cracks from pavement images remains challenging, due to factors such as low contrast and hair-like thinness. This study investigates key factors affecting segmentation performance and proposes a novel deep learning architecture designed to enhance segmentation robustness under these challenging conditions. The proposed model integrates a multi-resolution feature extraction stream with gated attention mechanisms to improve spatial awareness and selectively fuse information across feature levels. Our extensive experiments on a 3D pavement dataset demonstrated that the proposed method outperformed several state-of-the-art architectures, including FCN, U-Net, DeepLab, DeepCrack, and CrackFormer. Compared with U-Net, it improved F1 from 0.733 to 0.780. The gains were most pronounced on thin cracks, with F1 from 0.531 to 0.626. Our paired t -tests across folds showed the method is statistically better than U-Net and DeepCrack on Recall, IoU, Dice, and F1. These findings highlight the effectiveness of the attention-guided, multi-scale feature fusion method for robust crack segmentation using 3D pavement data.

Keywords: deep learning; U-Net; pavement distress segmentation; multi-resolution; gated attention (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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