{"title":"基于交叉尺度重叠斑块的道路裂缝检测关注网络","authors":"Po-Hao Chen;Jun-Wei Hsieh;Yi-Kuan Hsieh;Chuan-Wang Chang;Deng-Yuan Huang","doi":"10.1109/TITS.2025.3558279","DOIUrl":null,"url":null,"abstract":"Cracks on road surfaces pose serious risks to both pedestrians and drivers. Traditional manual crack detection methods are not only slow but also pose safety risks. Automating this process has the potential to greatly enhance detection efficiency and consequently improve driving safety. Although previous methods have shown promise in road crack detection, they often neglect interactions between multiple scales, causing smaller cracks to be overlooked in later stages of detection. This paper introduces the Cross-scale Overlapping Patch-based attention Network (COP-Net), which incorporates two critical components: the Scale-aware Channel Attention (SCA) module and the Patch-based Cross-scale Attention (PCA) module for crack detection. These innovations enable dynamic inference on multiple scales, resulting in a significant improvement in crack detection and segmentation. Notably, our approach excels at detecting both small and large cracks simultaneously. To validate the effectiveness of our approach, we conducted evaluations on three open datasets: CRACK500, CFD, and AEL. These evaluation results demonstrate that COP-Net surpasses eleven comparison methods, including HED, DeepCrack, UHDN, SSGNet, MFANet, FPHBN, DeepCrack, PBNet, PAFNet, CarNet, and SegFormer. Our model achieves new State-of-The-Art (SoTA) performance levels in terms of segmentation metrics such as AIU, ODS, and OIS.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 6","pages":"7587-7599"},"PeriodicalIF":8.4000,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cross-Scale Overlapping Patch-Based Attention Network for Road Crack Detection\",\"authors\":\"Po-Hao Chen;Jun-Wei Hsieh;Yi-Kuan Hsieh;Chuan-Wang Chang;Deng-Yuan Huang\",\"doi\":\"10.1109/TITS.2025.3558279\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cracks on road surfaces pose serious risks to both pedestrians and drivers. Traditional manual crack detection methods are not only slow but also pose safety risks. Automating this process has the potential to greatly enhance detection efficiency and consequently improve driving safety. Although previous methods have shown promise in road crack detection, they often neglect interactions between multiple scales, causing smaller cracks to be overlooked in later stages of detection. This paper introduces the Cross-scale Overlapping Patch-based attention Network (COP-Net), which incorporates two critical components: the Scale-aware Channel Attention (SCA) module and the Patch-based Cross-scale Attention (PCA) module for crack detection. These innovations enable dynamic inference on multiple scales, resulting in a significant improvement in crack detection and segmentation. Notably, our approach excels at detecting both small and large cracks simultaneously. To validate the effectiveness of our approach, we conducted evaluations on three open datasets: CRACK500, CFD, and AEL. These evaluation results demonstrate that COP-Net surpasses eleven comparison methods, including HED, DeepCrack, UHDN, SSGNet, MFANet, FPHBN, DeepCrack, PBNet, PAFNet, CarNet, and SegFormer. Our model achieves new State-of-The-Art (SoTA) performance levels in terms of segmentation metrics such as AIU, ODS, and OIS.\",\"PeriodicalId\":13416,\"journal\":{\"name\":\"IEEE Transactions on Intelligent Transportation Systems\",\"volume\":\"26 6\",\"pages\":\"7587-7599\"},\"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/10972128/\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Intelligent Transportation Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10972128/","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}
Cross-Scale Overlapping Patch-Based Attention Network for Road Crack Detection
Cracks on road surfaces pose serious risks to both pedestrians and drivers. Traditional manual crack detection methods are not only slow but also pose safety risks. Automating this process has the potential to greatly enhance detection efficiency and consequently improve driving safety. Although previous methods have shown promise in road crack detection, they often neglect interactions between multiple scales, causing smaller cracks to be overlooked in later stages of detection. This paper introduces the Cross-scale Overlapping Patch-based attention Network (COP-Net), which incorporates two critical components: the Scale-aware Channel Attention (SCA) module and the Patch-based Cross-scale Attention (PCA) module for crack detection. These innovations enable dynamic inference on multiple scales, resulting in a significant improvement in crack detection and segmentation. Notably, our approach excels at detecting both small and large cracks simultaneously. To validate the effectiveness of our approach, we conducted evaluations on three open datasets: CRACK500, CFD, and AEL. These evaluation results demonstrate that COP-Net surpasses eleven comparison methods, including HED, DeepCrack, UHDN, SSGNet, MFANet, FPHBN, DeepCrack, PBNet, PAFNet, CarNet, and SegFormer. Our model achieves new State-of-The-Art (SoTA) performance levels in terms of segmentation metrics such as AIU, ODS, and OIS.
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.