{"title":"Semi-Conv-DETR:集成卷积增强和半监督 DETR 的铁路道碴床缺陷检测模型","authors":"","doi":"10.1016/j.trgeo.2024.101334","DOIUrl":null,"url":null,"abstract":"<div><p>Railway ballast bed defects, including subsidence, mud pumping, and abnormal water, pose significant safety risks by destabilizing the railway ballast beds. Timely detection and repair of railway ballast bed defects are vital for safeguarding the security of both the trains and their passengers. Ground-Penetrating Radar (GPR) is widely used for railway ballast beds inspection and evaluation owing to its high speed and non-destructive characteristics. However, GPR image data contain considerable noise, and the distinct shapes and sizes of each ballast bed defect renders it challenging to apply a unified data annotation standard, which hampers the development of railway ballast bed defect detection models. Considering the distinct wave-like characteristics of GPR data and the vaguely contours of the defects to be identified, we propose a convolutional augmentation operation tailored for GPR images. Furthermore, we also investigate Semi-Supervised Learning by employing limited annotated railway ballast bed inspection data along with a vast amount of unlabeled data to joint train the DETR detection model. To sum up, we proposed a semi-supervised DETR model supplemented with convolutional augmentation for railway ballast bed defect detection, termed as Semi-Conv-DETR model. Experimental outcomes indicate that Semi-Conv-DETR shows an improvement of 58.6 % in accuracy when compared to the classical Faster-RCNN model.</p></div>","PeriodicalId":56013,"journal":{"name":"Transportation Geotechnics","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Semi-Conv-DETR: A railway ballast bed defect detection model integrating convolutional augmentation and semi-supervised DETR\",\"authors\":\"\",\"doi\":\"10.1016/j.trgeo.2024.101334\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Railway ballast bed defects, including subsidence, mud pumping, and abnormal water, pose significant safety risks by destabilizing the railway ballast beds. Timely detection and repair of railway ballast bed defects are vital for safeguarding the security of both the trains and their passengers. Ground-Penetrating Radar (GPR) is widely used for railway ballast beds inspection and evaluation owing to its high speed and non-destructive characteristics. However, GPR image data contain considerable noise, and the distinct shapes and sizes of each ballast bed defect renders it challenging to apply a unified data annotation standard, which hampers the development of railway ballast bed defect detection models. Considering the distinct wave-like characteristics of GPR data and the vaguely contours of the defects to be identified, we propose a convolutional augmentation operation tailored for GPR images. Furthermore, we also investigate Semi-Supervised Learning by employing limited annotated railway ballast bed inspection data along with a vast amount of unlabeled data to joint train the DETR detection model. To sum up, we proposed a semi-supervised DETR model supplemented with convolutional augmentation for railway ballast bed defect detection, termed as Semi-Conv-DETR model. Experimental outcomes indicate that Semi-Conv-DETR shows an improvement of 58.6 % in accuracy when compared to the classical Faster-RCNN model.</p></div>\",\"PeriodicalId\":56013,\"journal\":{\"name\":\"Transportation Geotechnics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-08-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Geotechnics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214391224001557\",\"RegionNum\":2,\"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":"Transportation Geotechnics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214391224001557","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
Semi-Conv-DETR: A railway ballast bed defect detection model integrating convolutional augmentation and semi-supervised DETR
Railway ballast bed defects, including subsidence, mud pumping, and abnormal water, pose significant safety risks by destabilizing the railway ballast beds. Timely detection and repair of railway ballast bed defects are vital for safeguarding the security of both the trains and their passengers. Ground-Penetrating Radar (GPR) is widely used for railway ballast beds inspection and evaluation owing to its high speed and non-destructive characteristics. However, GPR image data contain considerable noise, and the distinct shapes and sizes of each ballast bed defect renders it challenging to apply a unified data annotation standard, which hampers the development of railway ballast bed defect detection models. Considering the distinct wave-like characteristics of GPR data and the vaguely contours of the defects to be identified, we propose a convolutional augmentation operation tailored for GPR images. Furthermore, we also investigate Semi-Supervised Learning by employing limited annotated railway ballast bed inspection data along with a vast amount of unlabeled data to joint train the DETR detection model. To sum up, we proposed a semi-supervised DETR model supplemented with convolutional augmentation for railway ballast bed defect detection, termed as Semi-Conv-DETR model. Experimental outcomes indicate that Semi-Conv-DETR shows an improvement of 58.6 % in accuracy when compared to the classical Faster-RCNN model.
期刊介绍:
Transportation Geotechnics is a journal dedicated to publishing high-quality, theoretical, and applied papers that cover all facets of geotechnics for transportation infrastructure such as roads, highways, railways, underground railways, airfields, and waterways. The journal places a special emphasis on case studies that present original work relevant to the sustainable construction of transportation infrastructure. The scope of topics it addresses includes the geotechnical properties of geomaterials for sustainable and rational design and construction, the behavior of compacted and stabilized geomaterials, the use of geosynthetics and reinforcement in constructed layers and interlayers, ground improvement and slope stability for transportation infrastructures, compaction technology and management, maintenance technology, the impact of climate, embankments for highways and high-speed trains, transition zones, dredging, underwater geotechnics for infrastructure purposes, and the modeling of multi-layered structures and supporting ground under dynamic and repeated loads.