Xin Guo;Wenzhong Tang;Haoran Wang;Jiale Wang;Shuai Wang;Xiaolei Qu;Xun Lin
{"title":"MorFormer: Morphology-Aware Transformer for Generalized Pavement Crack Segmentation","authors":"Xin Guo;Wenzhong Tang;Haoran Wang;Jiale Wang;Shuai Wang;Xiaolei Qu;Xun Lin","doi":"10.1109/TITS.2025.3558782","DOIUrl":null,"url":null,"abstract":"Cracks are common on pavements. Accurate crack detection plays a vital role in pavement maintenance. However, cracks have rich and varied morphological features and fine edges, making this task challenging. Additionally, noise factors such as stains, scratches, and complex textures in the pavement background can easily be confused with cracks, increasing the risk of false prediction in the segmentation process. Therefore, we propose Background Morphology Learning (BML) to reconstruct morphological features of the pavement background noise, extract background morphological dissimilarity maps to suppress interference and reduce false alarms. In addition, we propose Crack Morphology-aware Attention (CMA), which adaptively learns the morphological shape of cracks and dynamically adjusts the shape of the attention receptive field to the topological features of the cracks. This significantly improves the completeness of segmentation. Our method mitigates the problems of false alarms and incomplete segmentation results in the crack segmentation task. Therefore, we propose a Morphology-Aware Transformer (MorFormer) that achieves state-of-the-art results on five public datasets. Moreover, we propose a large-scale cross-domain benchmark for crack segmentation, where MorFormer exhibits excellent domain generalization.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"8219-8232"},"PeriodicalIF":8.4000,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Intelligent Transportation Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10971963/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
Abstract
Cracks are common on pavements. Accurate crack detection plays a vital role in pavement maintenance. However, cracks have rich and varied morphological features and fine edges, making this task challenging. Additionally, noise factors such as stains, scratches, and complex textures in the pavement background can easily be confused with cracks, increasing the risk of false prediction in the segmentation process. Therefore, we propose Background Morphology Learning (BML) to reconstruct morphological features of the pavement background noise, extract background morphological dissimilarity maps to suppress interference and reduce false alarms. In addition, we propose Crack Morphology-aware Attention (CMA), which adaptively learns the morphological shape of cracks and dynamically adjusts the shape of the attention receptive field to the topological features of the cracks. This significantly improves the completeness of segmentation. Our method mitigates the problems of false alarms and incomplete segmentation results in the crack segmentation task. Therefore, we propose a Morphology-Aware Transformer (MorFormer) that achieves state-of-the-art results on five public datasets. Moreover, we propose a large-scale cross-domain benchmark for crack segmentation, where MorFormer exhibits excellent domain generalization.
期刊介绍:
The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.