Asymmetric dual-decoder-U-Net for pavement crack semantic segmentation

Abstract

Accurate pavement crack segmentation is crucial for civil engineering and infrastructure maintenance. To address the challenge of imbalanced data resulting from the prevalence of non-crack pixels, this research seeks to improve the quality of pavement crack segmentation, particularly for thick and tiny cracks. This paper presents an Asymmetric Dual-Decoder-U-Net (ADDU-Net) model, which involves constructing an asymmetric dual decoder with a dual attention module to better capture the features of both thick and tiny cracks under diverse environmental conditions. Through evaluation with images from four benchmark datasets, the ADDU-Net model demonstrates its effectiveness and robustness in accurately segmenting various types of cracks. This segmentation model shows significant potential for improving crack segmentation in industrial applications.