Sibo Huang;Guijie Zhu;Jiaming Tang;Weixiong Li;Zhun Fan
{"title":"路面地下目标探地雷达图像多视角语义分割","authors":"Sibo Huang;Guijie Zhu;Jiaming Tang;Weixiong Li;Zhun Fan","doi":"10.1109/TITS.2025.3559498","DOIUrl":null,"url":null,"abstract":"Effective infrastructure health monitoring is crucial within transportation cyber-physical systems, where accurate road health detection is vital for ensuring road safety and the stability of intelligent transportation systems. To address the challenges of identifying pavement subsurface objects using 3D ground penetrating radar (GPR) data, we propose a multi-perspective cascading recognition method that integrates B-scan and C-scan images. This method is built on a lightweight dual-stream semantic segmentation model called AttnGPRNet, developed in this work to improve feature extraction through attention mechanisms and enhance subsurface object recognition. Initially, the model segments B-scan images to identify potential target regions, followed by more precise segmentation of 3L-C-scan images based on preliminary results. Additionally, we constructed a multi-view dataset using 3D GPR scans from over 100 kilometers of urban roads and evaluated the effectiveness of the proposed method through experiments. Experimental results show that our model outperforms existing advanced methods, achieving mIoU of 78.80% and 83.96% on B-scan and 3L-C-scan, and F1 scores of 87.65% and 91.07%, respectively. Moreover, the method has been deployed in Xiaoning Road GPR image intelligent recognition system and verified through on-site drilling, demonstrating its practical potential for road health monitoring.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 9","pages":"14339-14352"},"PeriodicalIF":8.4000,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-Perspective Semantic Segmentation of Ground Penetrating Radar Images for Pavement Subsurface Objects\",\"authors\":\"Sibo Huang;Guijie Zhu;Jiaming Tang;Weixiong Li;Zhun Fan\",\"doi\":\"10.1109/TITS.2025.3559498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Effective infrastructure health monitoring is crucial within transportation cyber-physical systems, where accurate road health detection is vital for ensuring road safety and the stability of intelligent transportation systems. To address the challenges of identifying pavement subsurface objects using 3D ground penetrating radar (GPR) data, we propose a multi-perspective cascading recognition method that integrates B-scan and C-scan images. This method is built on a lightweight dual-stream semantic segmentation model called AttnGPRNet, developed in this work to improve feature extraction through attention mechanisms and enhance subsurface object recognition. Initially, the model segments B-scan images to identify potential target regions, followed by more precise segmentation of 3L-C-scan images based on preliminary results. Additionally, we constructed a multi-view dataset using 3D GPR scans from over 100 kilometers of urban roads and evaluated the effectiveness of the proposed method through experiments. Experimental results show that our model outperforms existing advanced methods, achieving mIoU of 78.80% and 83.96% on B-scan and 3L-C-scan, and F1 scores of 87.65% and 91.07%, respectively. Moreover, the method has been deployed in Xiaoning Road GPR image intelligent recognition system and verified through on-site drilling, demonstrating its practical potential for road health monitoring.\",\"PeriodicalId\":13416,\"journal\":{\"name\":\"IEEE Transactions on Intelligent Transportation Systems\",\"volume\":\"26 9\",\"pages\":\"14339-14352\"},\"PeriodicalIF\":8.4000,\"publicationDate\":\"2025-04-23\",\"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/10975062/\",\"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/10975062/","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}
Multi-Perspective Semantic Segmentation of Ground Penetrating Radar Images for Pavement Subsurface Objects
Effective infrastructure health monitoring is crucial within transportation cyber-physical systems, where accurate road health detection is vital for ensuring road safety and the stability of intelligent transportation systems. To address the challenges of identifying pavement subsurface objects using 3D ground penetrating radar (GPR) data, we propose a multi-perspective cascading recognition method that integrates B-scan and C-scan images. This method is built on a lightweight dual-stream semantic segmentation model called AttnGPRNet, developed in this work to improve feature extraction through attention mechanisms and enhance subsurface object recognition. Initially, the model segments B-scan images to identify potential target regions, followed by more precise segmentation of 3L-C-scan images based on preliminary results. Additionally, we constructed a multi-view dataset using 3D GPR scans from over 100 kilometers of urban roads and evaluated the effectiveness of the proposed method through experiments. Experimental results show that our model outperforms existing advanced methods, achieving mIoU of 78.80% and 83.96% on B-scan and 3L-C-scan, and F1 scores of 87.65% and 91.07%, respectively. Moreover, the method has been deployed in Xiaoning Road GPR image intelligent recognition system and verified through on-site drilling, demonstrating its practical potential for road health monitoring.
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.