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

IF 4.9 2区 工程技术 Q1 ENGINEERING, CIVIL
Jing Teng , Xujie Long , Qi Yang , Guoqing Jing , Huan Liu
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引用次数: 0

摘要

铁路道碴道床缺陷,包括沉陷、抽泥和异常积水,会破坏铁路道碴道床的稳定性,从而构成重大安全风险。及时发现和修复铁路道碴缺陷对保障列车和乘客的安全至关重要。探地雷达(GPR)具有高速和无损的特点,因此被广泛用于铁路道碴检测和评估。然而,GPR 图像数据包含大量噪声,而且每个道碴床缺陷的形状和大小各不相同,因此很难应用统一的数据标注标准,这阻碍了铁路道碴床缺陷检测模型的开发。考虑到 GPR 数据明显的波状特征和待识别缺陷的模糊轮廓,我们提出了一种为 GPR 图像量身定制的卷积增强操作。此外,我们还研究了半监督学习(Semi-Supervised Learning),利用有限的有标注的铁路道床检测数据和大量无标注数据来联合训练 DETR 检测模型。总之,我们提出了一种辅以卷积增强的半监督 DETR 模型,用于铁路道碴床缺陷检测,称为 Semi-Conv-DETR 模型。实验结果表明,与经典的 Faster-RCNN 模型相比,Semi-Conv-DETR 的准确率提高了 58.6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Semi-Conv-DETR: A railway ballast bed defect detection model integrating convolutional augmentation and semi-supervised DETR

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.

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来源期刊
Transportation Geotechnics
Transportation Geotechnics Social Sciences-Transportation
CiteScore
8.10
自引率
11.30%
发文量
194
审稿时长
51 days
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.
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